The document proposes an attribute-assisted reranking model for web image search. It uses semantic attributes along with visual features to represent images, constructs a hypergraph to model relationships between images, and performs hypergraph ranking to reorder search results. Attributes provide an intermediate-level semantic description that can help reduce the semantic gap between low-level visual features and high-level meanings. The proposed approach extracts both visual and attribute features from initial search results, builds a hypergraph integrating the two types of features, and learns relevance scores to rerank the images.
An attribute assisted reranking model for web image searchShakas Technologies
Image search reranking is an effective approach to refine the text-based image search result. Most existing reranking approaches are based on low-level visual features. In this paper, we propose to exploit semantic attributes for image search reranking. Based on the classifiers for all the predefined attributes, each image is represented by an attribute feature consisting of the responses from these classifiers
The document proposes an attribute-assisted reranking model for web image search. It uses semantic attributes to refine image search results from a text-based search engine. A hypergraph is used to model relationships between images by integrating low-level visual features and attribute features. A visual-attribute joint hypergraph learning approach simultaneously explores these two information sources to reorder images based on their relationships and attributes. The proposed approach is shown to outperform other reranking methods in experiments.
The document proposes a method to re-rank images returned from an image search engine by incorporating visual similarity. It extracts interest points from images to determine visual content. Images are then re-ranked based on visual similarity, as determined by comparing interest points. A graph model is generated to represent visual similarities between images as links. PageRank is then applied to the graph to assign priority scores to images, with more visually similar images being ranked higher. The goal is to return images that are both relevant and visually diverse.
An Attribute-Assisted Reranking Model for Web Image Search1crore projects
IEEE PROJECTS 2015
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Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
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13. LAB VIEW
14. Multi Sim
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Tamin Nadu, INDIA - 600 026
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website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
This document describes a system for image retrieval and annotation using both image content and tags. The system builds a unified graph fusing an image similarity graph based on visual features with an image-tag bipartite graph. It uses a combination of content-based image retrieval (CBIR) and text-based image retrieval (TBIR) with a fusion parameter to balance the influence of image contents and tags. The system is able to retrieve images related to a query as well as annotate the query image. It extracts color, shape, and texture features from images and combines these features for matching between database images and queries.
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING sipij
The aim of this paper is to present a comparative study of two linear dimension reduction methods namely
PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to
transform the high dimensional input space onto the feature space where the maximal variance is
displayed. The feature selection in traditional LDA is obtained by maximizing the difference between
classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the
whole data set where LDA tries to find the axes for best class seperability. The neural network is trained
about the reduced feature set (using PCA or LDA) of images in the database for fast searching of images
from the database using back propagation algorithm. The proposed method is experimented over a general
image database using Matlab. The performance of these systems has been evaluated by Precision and
Recall measures. Experimental results show that PCA gives the better performance in terms of higher
precision and recall values with lesser computational complexity than LDA
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...csandit
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing the distance within classes. PCA
finds the axes with maximum variance for the whole
data set where LDA tries to find the axes
for best class seperability. The proposed method is
experimented over a general image database
using Matlab. The performance of these systems has
been evaluated by Precision and Recall
measures. Experimental results show that PCA based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
An attribute assisted reranking model for web image searchShakas Technologies
Image search reranking is an effective approach to refine the text-based image search result. Most existing reranking approaches are based on low-level visual features. In this paper, we propose to exploit semantic attributes for image search reranking. Based on the classifiers for all the predefined attributes, each image is represented by an attribute feature consisting of the responses from these classifiers
The document proposes an attribute-assisted reranking model for web image search. It uses semantic attributes to refine image search results from a text-based search engine. A hypergraph is used to model relationships between images by integrating low-level visual features and attribute features. A visual-attribute joint hypergraph learning approach simultaneously explores these two information sources to reorder images based on their relationships and attributes. The proposed approach is shown to outperform other reranking methods in experiments.
The document proposes a method to re-rank images returned from an image search engine by incorporating visual similarity. It extracts interest points from images to determine visual content. Images are then re-ranked based on visual similarity, as determined by comparing interest points. A graph model is generated to represent visual similarities between images as links. PageRank is then applied to the graph to assign priority scores to images, with more visually similar images being ranked higher. The goal is to return images that are both relevant and visually diverse.
An Attribute-Assisted Reranking Model for Web Image Search1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
This document describes a system for image retrieval and annotation using both image content and tags. The system builds a unified graph fusing an image similarity graph based on visual features with an image-tag bipartite graph. It uses a combination of content-based image retrieval (CBIR) and text-based image retrieval (TBIR) with a fusion parameter to balance the influence of image contents and tags. The system is able to retrieve images related to a query as well as annotate the query image. It extracts color, shape, and texture features from images and combines these features for matching between database images and queries.
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING sipij
The aim of this paper is to present a comparative study of two linear dimension reduction methods namely
PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to
transform the high dimensional input space onto the feature space where the maximal variance is
displayed. The feature selection in traditional LDA is obtained by maximizing the difference between
classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the
whole data set where LDA tries to find the axes for best class seperability. The neural network is trained
about the reduced feature set (using PCA or LDA) of images in the database for fast searching of images
from the database using back propagation algorithm. The proposed method is experimented over a general
image database using Matlab. The performance of these systems has been evaluated by Precision and
Recall measures. Experimental results show that PCA gives the better performance in terms of higher
precision and recall values with lesser computational complexity than LDA
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...csandit
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing the distance within classes. PCA
finds the axes with maximum variance for the whole
data set where LDA tries to find the axes
for best class seperability. The proposed method is
experimented over a general image database
using Matlab. The performance of these systems has
been evaluated by Precision and Recall
measures. Experimental results show that PCA based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
Implementation of Fuzzy Logic for the High-Resolution Remote Sensing Images w...IOSR Journals
This document describes an implementation of fuzzy logic for high-resolution remote sensing image classification with improved accuracy. It discusses using an object-based approach with fuzzy rules to classify urban land covers in a satellite image. The approach involves image segmentation using k-means clustering or ISODATA clustering. Features are then extracted from the image objects and fuzzy logic is applied to classify the objects based on membership functions. The method was tested on different sensor and resolution images in MATLAB and showed improved classification accuracy over other techniques, achieving lower entropy in results. Future work planned includes designing an unsupervised classification model combining k-means clustering and fuzzy-based object orientation.
A NOVEL WEB IMAGE RE-RANKING APPROACH BASED ON QUERY SPECIFIC SEMANTIC SIGNAT...Journal For Research
Image re-ranking, is an effective way to improve the results of web-based image search. Given a query keyword, a pool of images are initailly retrieved primarily based on textual data, the remaining images are re-ranked based on their visual similarities with the query image corresponding to the user input. A major challenge is that the similarities of visual features don't well correlate with images’ semantic meanings that interpret users’ search intention. Recently people proposed to match pictures in a semantic space that used attributes or reference categories closely associated with the semantic meanings of images as basis. Even though, learning a universal visual semantic space to characterize extremely diverse images from the internet is troublesome and inefficient. In this thesis, we propose a completely distinctive image re-ranking framework that learns completely different semantic spaces for numerous query keywords automatically at the on-line stage. The visual features of images are projected into their corresponding semantic spaces to induce semantic signatures. At the online stage, images are re-ranked by scrutiny their semantic signatures obtained from the semantic spaces such that by the query keyword. The proposed query-specific semantic signatures considerably improve both the accuracy and efficiency of image re-ranking.
A novel Image Retrieval System using an effective region based shape represen...CSCJournals
With recent improvements in methods for the acquisition and rendering of shapes, the need for retrieval of shapes from large repositories of shapes has gained prominence. A variety of methods have been proposed that enable the efficient querying of shape repositories for a desired shape or image. Many of these methods use a sample shape as a query and attempt to retrieve shapes from the database that have a similar shape. This paper introduces a novel and efficient shape matching approach for the automatic identification of real world objects. The identification process is applied on isolated objects and requires the segmentation of the image into separate objects, followed by the extraction of representative shape signatures and the similarity estimation of pairs of objects considering the information extracted from the segmentation process and shape signature. We compute a 1D shape signature function from a region shape and use it for region shape representation and retrieval through similarity estimation. The proposed region shape feature is much more efficient to compute than other region shape techniques invariant to image transformation.
The content based Image Retrieval is the restoration of images with respect to the visual appearances
like texture, shape and color.The methods, components and the algorithms adopted in this content based
retrieval of images were commonly derived from the areas like pattern identification, signal progressing
and the computer vision. Moreover the shape and the color features were abstracted in the course of
wavelet transformation and color histogram. Thus the new content based retrieval is proposed in this
research paper.In this paper the algorithms were required to propose with regards to the shape, shade and
texture feature abstraction .The concept of discrete wavelet transform to be implemented in order to
compute the Euclidian distance.The calculation of clusters was made with the help of the modified KMeans
clustering technique. Thus the analysis is made in among the query image and the database
image.The MATLAB software is implemented to execute the queries. The K-Means of abstraction is
proposed by performing fragmentation and grid-means module, feature extraction and K- nearest neighbor
clustering algorithms to construct the content based image retrieval system.Thus the obtained result are
made to compute and compared to all other algorithm for the retrieval of quality image features
Comparison of Various Web Image Re - Ranking TechniquesIJSRD
Image re-ranking, is an quite an efficient way to improve the results that are fetched from the web-based image search query. Given a query keyword for the image, a pool of images are first retrieved based on textual information, then the images are re-ranked based on their visual similarities with the query image according to the user input. But when, the images’ visual features do not match with the semantic meanings of the users’ entered query or keyword, it becomes a major challenge to make available the actual searched image. Hence, in this paper, the various Web image Re- ranking techniques are studied, on how it approaches towards the Web Image search that the user has input in query.
Efficient Image Retrieval by Multi-view Alignment Technique with Non Negative...RSIS International
The biggest challenge in today’s world is a searching of
images in a large database. For searching of an image a
technique which can be termed as Hashing is used. Already there
are many hashing techniques are present for retrieval of an
image in a large databank. The hashing technique can be done
on images by considering the high dimensional descriptor of an
image but in the existing hashing techniques single dimensional
descriptor is used from this the performance of the probability of
distribution of an image search will not achieve as expected. And
the drawback is that giving the query input in a texture format
leads to the limitations of image search that is firstly due to the
limited keyword and the second is Annotation approach by
human is ambiguous and incomplete.
To overcome from these drawbacks a new technique has been
proposed named as Multiview Alignment Hashing technique in
which it keeps the high dimensional feature descriptor data as
well probability of distribution of an images in a database. Along
with the Multiview feature descriptor another technique can be
used that is Nonnegetive Matrix Factorization (NMF). NMF is a
popular technique used in data mining in which clustering of a
data will be takes place by considering only the non- negative
matrix value.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Design of an Effective Method for Image RetrievalAM Publications
This document summarizes a research paper that proposes an effective method for image retrieval using relevance feedback with self-organizing maps (SOMs). The key points are:
1) Relevance feedback is used to improve image retrieval performance by iteratively refining queries based on user feedback on retrieved images. With SOMs, relevance feedback scores are spread to neighboring image clusters.
2) The method represents images with features and maps them to units in a SOM grid. Positive and negative feedback is scored on the grid. Scores are spread to neighboring units based on their distance in feature space.
3) Images are retrieved by selecting from high-scoring areas of the SOM grid, using either a depth-
The document reviews various feature extraction techniques that have been used for content-based image retrieval (CBIR) systems. It discusses several approaches for extracting color, texture, shape and spatial features from images. It also examines different similarity measures and evaluation methods for CBIR systems, including precision, recall and distance metrics. Feature extraction is a key factor for CBIR, and the paper provides an overview of some of the major techniques that have been explored for this task.
Implementation of High Dimension Colour Transform in Domain of Image ProcessingIRJET Journal
This document discusses implementing a high-dimensional color transform method for salient region detection in images. It aims to extract salient regions by designing a saliency map using global and local image features. The creation of the saliency map involves mapping colors from RGB space to a high-dimensional color space to find an optimal linear combination of color coefficients. This allows composing an accurate saliency map. The performance is further improved by using relative location and color contrast between superpixels as features and resolving the saliency estimation from an initial trimap using a learning-based algorithm. The method is analyzed on a dataset of training images.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Scene Description From Images To SentencesIRJET Journal
This document presents an approach for generating sentences to describe images using distributed intelligence. It involves detecting objects in images using YOLO detection, finding relative positions of objects, labeling background scenes, generating tuples of objects/scenes/relations, extracting candidate sentences from Wikipedia containing tuple elements, searching images for each sentence and selecting the sentence whose images most closely match the input image. The approach is compared to the Babytalk model using BLEU and ROUGE scores, showing comparable performance. Future work to improve object detection and use larger knowledge sources is discussed.
This document discusses optimizing content-based image retrieval in peer-to-peer systems. It summarizes previous work on content-based image retrieval using multi-instance queries in peer-to-peer networks. The authors propose two optimizations to previous work: 1) clustering peers to reduce search time, and 2) constructing a search index at cluster heads to avoid searching each peer. Their experiments show the proposed approach reduces search time compared to previous work, with some reduction in accuracy that improves as more nodes are added. The authors plan future work to analyze performance with different cluster sizes and representations for faster search.
A Novel Approach of Fuzzy Based Semi-Automatic Annotation for Similar Facial ...ijsrd.com
This paper proposes a semi-automatic approach for annotating similar facial images that are often weakly labeled with duplicate, noisy, or incomplete names. It uses an unsupervised label refinement (ULR) algorithm with fuzzy clustering to improve the labels. The ULR algorithm refines the labels through multiple iterations using machine learning techniques. It also uses a parallel computation framework to solve very large problems efficiently. Evaluation on a dataset with introduced noise shows the proposed optimized fuzzy ULR approach outperforms other ULR algorithms in refining the labels.
IRJET-Semi-Supervised Collaborative Image Retrieval using Relevance FeedbackIRJET Journal
This document describes a semi-supervised collaborative image retrieval system using relevance feedback. The system aims to improve the performance of content-based image retrieval (CBIR) systems by reducing the number of images a user must label during relevance feedback. It uses a semi-supervised approach where the user only needs to label a few of the most informative images. These labeled images are used to train a support vector machine (SVM) classifier. The images in the database are then resorted based on a new similarity metric determined by the classifier. The system provides iteratively improved retrieval results until the user is satisfied, thereby bridging the semantic gap between low-level visual features and high-level semantics.
This document summarizes an article that proposes a new approach to improving image web search techniques. It begins with an abstract that describes how rapidly increasing internet users and multimedia search are increasing network traffic, with most traffic being for searching multimedia content online. It then provides background on existing issues with image search and commonly used algorithms. The document proposes a hybrid image search engine that searches by image-to-image comparison, text/keywords, and uses a hybrid matches graph and time sensitivity filter to improve results. It describes the training and testing phases, featuring feature extraction and annotation matching to retrieve relevant images from queries.
A Review on Matching For Sketch TechniqueIOSR Journals
This document summarizes several techniques for sketch-based image retrieval. It discusses methods using SIFT features, HOG descriptors, color segmentation, and gradient orientation histograms. It also reviews applications of these techniques to domains like facial recognition, graffiti matching, and tattoo identification for law enforcement. The techniques aim to extract visual features from sketches that can be used to match and retrieve similar images from databases. While achieving good results, the methods have limitations regarding database size and specificity, and accuracy with complex textures and shapes. Overall, the review examines advances in using sketches as queries for image retrieval.
A feature selection method for automatic image annotationinventionjournals
ABSTRACT: Automatic image annotation (AIA) is the bridge of high-level semantic information and the low-level feature. AIA is an effective method to resolve the problem of “Semantic Gap”. According to the intrinsic character of AIA, some common features are selected from the labeled images by multiple instance learning method. The feature selection method is applied into the task of automatic image annotation in this paper. Each keyword is analyzed hierarchically in low-granularity-level under the framework of feature selection. Through the common representative instances are mined, the semantic similarity of images can be effectively expressed and the better annotation results are able to be acquired, which testifies the effectiveness of the proposed annotation method. Gaussian mixture model is built by the selected feature method to characterize the labeled keyword. The experimental results illustrate the good perfromance of AIA.
This document provides a review of different techniques for image retrieval from large databases, including text-based image retrieval and content-based image retrieval (CBIR). CBIR uses visual features extracted from images like color, texture, and shape to search for similar images. The document discusses some limitations of CBIR and proposes video-based image retrieval as a new direction. It also surveys recent research in areas like feature extraction, indexing, and discusses future directions like reducing the semantic gap between low-level features and high-level meanings.
An Impact on Content Based Image Retrival A Perspective Viewijtsrd
The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content based image retrieval CBIR , which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content based image retrieval in the last decade. We conclude with several promising directions for future research. Shivanshu Jaiswal | Dr. Avinash Sharma ""An Impact on Content Based Image Retrival: A Perspective View"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd29969.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/29969/an-impact-on-content-based-image-retrival-a-perspective-view/shivanshu-jaiswal
An Exploratory Study Exploiting Image Features For Term Assignment In MedicineStephen Faucher
This summary provides an overview of an exploratory study that examines using image features to assign controlled medical vocabulary terms (MeSH terms) to medical images:
- The study aims to determine how effective exploiting image content features can be for assigning index terms to medical images, as manual assignment is time-consuming. It analyzes whether similar image content implies similar term assignment.
- Preliminary results are reported from clustering a set of medical images based on visual features like color, texture, and shape. The study examines if images clustered as similar should inherit the terms of nearby existing images.
- The objectives are to discover how well content similarity correlates with term similarity, and to identify a threshold for content similarity beyond which term
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
Implementation of Fuzzy Logic for the High-Resolution Remote Sensing Images w...IOSR Journals
This document describes an implementation of fuzzy logic for high-resolution remote sensing image classification with improved accuracy. It discusses using an object-based approach with fuzzy rules to classify urban land covers in a satellite image. The approach involves image segmentation using k-means clustering or ISODATA clustering. Features are then extracted from the image objects and fuzzy logic is applied to classify the objects based on membership functions. The method was tested on different sensor and resolution images in MATLAB and showed improved classification accuracy over other techniques, achieving lower entropy in results. Future work planned includes designing an unsupervised classification model combining k-means clustering and fuzzy-based object orientation.
A NOVEL WEB IMAGE RE-RANKING APPROACH BASED ON QUERY SPECIFIC SEMANTIC SIGNAT...Journal For Research
Image re-ranking, is an effective way to improve the results of web-based image search. Given a query keyword, a pool of images are initailly retrieved primarily based on textual data, the remaining images are re-ranked based on their visual similarities with the query image corresponding to the user input. A major challenge is that the similarities of visual features don't well correlate with images’ semantic meanings that interpret users’ search intention. Recently people proposed to match pictures in a semantic space that used attributes or reference categories closely associated with the semantic meanings of images as basis. Even though, learning a universal visual semantic space to characterize extremely diverse images from the internet is troublesome and inefficient. In this thesis, we propose a completely distinctive image re-ranking framework that learns completely different semantic spaces for numerous query keywords automatically at the on-line stage. The visual features of images are projected into their corresponding semantic spaces to induce semantic signatures. At the online stage, images are re-ranked by scrutiny their semantic signatures obtained from the semantic spaces such that by the query keyword. The proposed query-specific semantic signatures considerably improve both the accuracy and efficiency of image re-ranking.
A novel Image Retrieval System using an effective region based shape represen...CSCJournals
With recent improvements in methods for the acquisition and rendering of shapes, the need for retrieval of shapes from large repositories of shapes has gained prominence. A variety of methods have been proposed that enable the efficient querying of shape repositories for a desired shape or image. Many of these methods use a sample shape as a query and attempt to retrieve shapes from the database that have a similar shape. This paper introduces a novel and efficient shape matching approach for the automatic identification of real world objects. The identification process is applied on isolated objects and requires the segmentation of the image into separate objects, followed by the extraction of representative shape signatures and the similarity estimation of pairs of objects considering the information extracted from the segmentation process and shape signature. We compute a 1D shape signature function from a region shape and use it for region shape representation and retrieval through similarity estimation. The proposed region shape feature is much more efficient to compute than other region shape techniques invariant to image transformation.
The content based Image Retrieval is the restoration of images with respect to the visual appearances
like texture, shape and color.The methods, components and the algorithms adopted in this content based
retrieval of images were commonly derived from the areas like pattern identification, signal progressing
and the computer vision. Moreover the shape and the color features were abstracted in the course of
wavelet transformation and color histogram. Thus the new content based retrieval is proposed in this
research paper.In this paper the algorithms were required to propose with regards to the shape, shade and
texture feature abstraction .The concept of discrete wavelet transform to be implemented in order to
compute the Euclidian distance.The calculation of clusters was made with the help of the modified KMeans
clustering technique. Thus the analysis is made in among the query image and the database
image.The MATLAB software is implemented to execute the queries. The K-Means of abstraction is
proposed by performing fragmentation and grid-means module, feature extraction and K- nearest neighbor
clustering algorithms to construct the content based image retrieval system.Thus the obtained result are
made to compute and compared to all other algorithm for the retrieval of quality image features
Comparison of Various Web Image Re - Ranking TechniquesIJSRD
Image re-ranking, is an quite an efficient way to improve the results that are fetched from the web-based image search query. Given a query keyword for the image, a pool of images are first retrieved based on textual information, then the images are re-ranked based on their visual similarities with the query image according to the user input. But when, the images’ visual features do not match with the semantic meanings of the users’ entered query or keyword, it becomes a major challenge to make available the actual searched image. Hence, in this paper, the various Web image Re- ranking techniques are studied, on how it approaches towards the Web Image search that the user has input in query.
Efficient Image Retrieval by Multi-view Alignment Technique with Non Negative...RSIS International
The biggest challenge in today’s world is a searching of
images in a large database. For searching of an image a
technique which can be termed as Hashing is used. Already there
are many hashing techniques are present for retrieval of an
image in a large databank. The hashing technique can be done
on images by considering the high dimensional descriptor of an
image but in the existing hashing techniques single dimensional
descriptor is used from this the performance of the probability of
distribution of an image search will not achieve as expected. And
the drawback is that giving the query input in a texture format
leads to the limitations of image search that is firstly due to the
limited keyword and the second is Annotation approach by
human is ambiguous and incomplete.
To overcome from these drawbacks a new technique has been
proposed named as Multiview Alignment Hashing technique in
which it keeps the high dimensional feature descriptor data as
well probability of distribution of an images in a database. Along
with the Multiview feature descriptor another technique can be
used that is Nonnegetive Matrix Factorization (NMF). NMF is a
popular technique used in data mining in which clustering of a
data will be takes place by considering only the non- negative
matrix value.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Design of an Effective Method for Image RetrievalAM Publications
This document summarizes a research paper that proposes an effective method for image retrieval using relevance feedback with self-organizing maps (SOMs). The key points are:
1) Relevance feedback is used to improve image retrieval performance by iteratively refining queries based on user feedback on retrieved images. With SOMs, relevance feedback scores are spread to neighboring image clusters.
2) The method represents images with features and maps them to units in a SOM grid. Positive and negative feedback is scored on the grid. Scores are spread to neighboring units based on their distance in feature space.
3) Images are retrieved by selecting from high-scoring areas of the SOM grid, using either a depth-
The document reviews various feature extraction techniques that have been used for content-based image retrieval (CBIR) systems. It discusses several approaches for extracting color, texture, shape and spatial features from images. It also examines different similarity measures and evaluation methods for CBIR systems, including precision, recall and distance metrics. Feature extraction is a key factor for CBIR, and the paper provides an overview of some of the major techniques that have been explored for this task.
Implementation of High Dimension Colour Transform in Domain of Image ProcessingIRJET Journal
This document discusses implementing a high-dimensional color transform method for salient region detection in images. It aims to extract salient regions by designing a saliency map using global and local image features. The creation of the saliency map involves mapping colors from RGB space to a high-dimensional color space to find an optimal linear combination of color coefficients. This allows composing an accurate saliency map. The performance is further improved by using relative location and color contrast between superpixels as features and resolving the saliency estimation from an initial trimap using a learning-based algorithm. The method is analyzed on a dataset of training images.
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Scene Description From Images To SentencesIRJET Journal
This document presents an approach for generating sentences to describe images using distributed intelligence. It involves detecting objects in images using YOLO detection, finding relative positions of objects, labeling background scenes, generating tuples of objects/scenes/relations, extracting candidate sentences from Wikipedia containing tuple elements, searching images for each sentence and selecting the sentence whose images most closely match the input image. The approach is compared to the Babytalk model using BLEU and ROUGE scores, showing comparable performance. Future work to improve object detection and use larger knowledge sources is discussed.
This document discusses optimizing content-based image retrieval in peer-to-peer systems. It summarizes previous work on content-based image retrieval using multi-instance queries in peer-to-peer networks. The authors propose two optimizations to previous work: 1) clustering peers to reduce search time, and 2) constructing a search index at cluster heads to avoid searching each peer. Their experiments show the proposed approach reduces search time compared to previous work, with some reduction in accuracy that improves as more nodes are added. The authors plan future work to analyze performance with different cluster sizes and representations for faster search.
A Novel Approach of Fuzzy Based Semi-Automatic Annotation for Similar Facial ...ijsrd.com
This paper proposes a semi-automatic approach for annotating similar facial images that are often weakly labeled with duplicate, noisy, or incomplete names. It uses an unsupervised label refinement (ULR) algorithm with fuzzy clustering to improve the labels. The ULR algorithm refines the labels through multiple iterations using machine learning techniques. It also uses a parallel computation framework to solve very large problems efficiently. Evaluation on a dataset with introduced noise shows the proposed optimized fuzzy ULR approach outperforms other ULR algorithms in refining the labels.
IRJET-Semi-Supervised Collaborative Image Retrieval using Relevance FeedbackIRJET Journal
This document describes a semi-supervised collaborative image retrieval system using relevance feedback. The system aims to improve the performance of content-based image retrieval (CBIR) systems by reducing the number of images a user must label during relevance feedback. It uses a semi-supervised approach where the user only needs to label a few of the most informative images. These labeled images are used to train a support vector machine (SVM) classifier. The images in the database are then resorted based on a new similarity metric determined by the classifier. The system provides iteratively improved retrieval results until the user is satisfied, thereby bridging the semantic gap between low-level visual features and high-level semantics.
This document summarizes an article that proposes a new approach to improving image web search techniques. It begins with an abstract that describes how rapidly increasing internet users and multimedia search are increasing network traffic, with most traffic being for searching multimedia content online. It then provides background on existing issues with image search and commonly used algorithms. The document proposes a hybrid image search engine that searches by image-to-image comparison, text/keywords, and uses a hybrid matches graph and time sensitivity filter to improve results. It describes the training and testing phases, featuring feature extraction and annotation matching to retrieve relevant images from queries.
A Review on Matching For Sketch TechniqueIOSR Journals
This document summarizes several techniques for sketch-based image retrieval. It discusses methods using SIFT features, HOG descriptors, color segmentation, and gradient orientation histograms. It also reviews applications of these techniques to domains like facial recognition, graffiti matching, and tattoo identification for law enforcement. The techniques aim to extract visual features from sketches that can be used to match and retrieve similar images from databases. While achieving good results, the methods have limitations regarding database size and specificity, and accuracy with complex textures and shapes. Overall, the review examines advances in using sketches as queries for image retrieval.
A feature selection method for automatic image annotationinventionjournals
ABSTRACT: Automatic image annotation (AIA) is the bridge of high-level semantic information and the low-level feature. AIA is an effective method to resolve the problem of “Semantic Gap”. According to the intrinsic character of AIA, some common features are selected from the labeled images by multiple instance learning method. The feature selection method is applied into the task of automatic image annotation in this paper. Each keyword is analyzed hierarchically in low-granularity-level under the framework of feature selection. Through the common representative instances are mined, the semantic similarity of images can be effectively expressed and the better annotation results are able to be acquired, which testifies the effectiveness of the proposed annotation method. Gaussian mixture model is built by the selected feature method to characterize the labeled keyword. The experimental results illustrate the good perfromance of AIA.
This document provides a review of different techniques for image retrieval from large databases, including text-based image retrieval and content-based image retrieval (CBIR). CBIR uses visual features extracted from images like color, texture, and shape to search for similar images. The document discusses some limitations of CBIR and proposes video-based image retrieval as a new direction. It also surveys recent research in areas like feature extraction, indexing, and discusses future directions like reducing the semantic gap between low-level features and high-level meanings.
An Impact on Content Based Image Retrival A Perspective Viewijtsrd
The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content based image retrieval CBIR , which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content based image retrieval in the last decade. We conclude with several promising directions for future research. Shivanshu Jaiswal | Dr. Avinash Sharma ""An Impact on Content Based Image Retrival: A Perspective View"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd29969.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/29969/an-impact-on-content-based-image-retrival-a-perspective-view/shivanshu-jaiswal
An Exploratory Study Exploiting Image Features For Term Assignment In MedicineStephen Faucher
This summary provides an overview of an exploratory study that examines using image features to assign controlled medical vocabulary terms (MeSH terms) to medical images:
- The study aims to determine how effective exploiting image content features can be for assigning index terms to medical images, as manual assignment is time-consuming. It analyzes whether similar image content implies similar term assignment.
- Preliminary results are reported from clustering a set of medical images based on visual features like color, texture, and shape. The study examines if images clustered as similar should inherit the terms of nearby existing images.
- The objectives are to discover how well content similarity correlates with term similarity, and to identify a threshold for content similarity beyond which term
The document summarizes a research paper that proposes the VisualRank approach for image retrieval from large-scale image databases. It describes extracting visual features like texture, color, and gray histograms from images. Images are ranked based on measuring similarity between these extracted features. K-means clustering is used to group similar images, and minimum distance is calculated to retrieve images with maximum similarity to the query image. The implementation and results of applying VisualRank to an image database are discussed, showing it can effectively retrieve relevant images.
The document summarizes a research paper that proposes the VisualRank approach for image retrieval from large-scale image databases. It describes extracting visual features like texture, color, and gray histograms from images. Images are ranked based on measuring similarity between these extracted features. K-means clustering is used to group similar images, and minimum distance is calculated to retrieve images with maximum similarity to the query image. The implementation and results of applying VisualRank to an image database are discussed, showing it can effectively retrieve relevant images based on visual feature matching.
Survey on Supervised Method for Face Image Retrieval Based on Euclidean Dist...Editor IJCATR
This document summarizes various supervised methods for face image retrieval based on Euclidean distance. It discusses literature on active shape models, principal component analysis, linear discriminant analysis, locality-constrained linear coding, bag-of-words models, local binary patterns, and support vector machines. It evaluates support vector machines as the best classifier for face image retrieval systems due to its ability to significantly reduce the need for labeled training data and accurately classify faces, proteins, and characters. The document concludes that a content-based face retrieval system using support vector machines improves detection performance by retrieving similar faces from a database based on Euclidean distance calculations between local binary pattern features of the query and database images.
Iaetsd enhancement of face retrival desigend forIaetsd Iaetsd
This document proposes two methods for enhancing content-based face image retrieval: attribute-enhanced sparse coding and attribute-embedded inverted indexing. Attribute-enhanced sparse coding uses human attributes to generate semantic-aware codewords during offline encoding. Attribute-embedded inverted indexing represents human attributes of query images as binary signatures for efficient online retrieval. Experimental results showed these methods reduced quantization error and improved face retrieval accuracy on public datasets, while maintaining scalability.
A Study on Image Retrieval Features and Techniques with Various CombinationsIRJET Journal
This document discusses image retrieval techniques for content-based image retrieval systems. It begins with an introduction to the growth of digital image collections and the need for large-scale image retrieval systems. It then reviews different features used for image retrieval, such as color histograms, color moments, color coherence vectors, and discrete wavelet transforms. Edge features and corner features are also discussed. The document concludes that using only one feature type such as color or texture is not sufficient, and the best approach is to extract multiple high-quality features and combine them for image retrieval.
This document provides a comprehensive review of recent developments in content-based image retrieval and feature extraction. It discusses various low-level visual features used for image retrieval, including color, texture, shape, and spatial features. It also reviews approaches that fuse low-level features and use local features. Machine learning and deep learning techniques for content-based image retrieval are also summarized. The document concludes by discussing open challenges and directions for future research in this area.
This document provides an overview of image feature detection, description, and matching techniques. It discusses the importance of these techniques for various computer vision applications and defines key concepts like local vs global features. The document also describes important properties of feature detectors like robustness and repeatability. It explains scale and affine invariance and provides examples of single-scale, multi-scale, and affine invariant feature detectors. Finally, it discusses evaluation of feature detection and description algorithms.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. The detected keywords are then used to index and retrieve similar textual images from two benchmark datasets. Experimental results show the approach effectively retrieves similar textual images by exploiting the dominant text information in the images.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. These keywords are then used to index and retrieve similar textual images based on the detected text. The experimental results on two benchmark datasets show this text-based approach is effective for retrieving textual images.
This document describes a proposed method to improve image classification accuracy and speed using the bag-of-features model with spatial pooling. The proposed method has two phases: a training phase to create an image feature database, and an evaluation phase to classify new images. In the evaluation phase, spatial pooling is applied to input image features before classification with KNN. Variance-based feature selection is also used to reduce features before KNN classification. Experimental results show the proposed method improves classification accuracy up to 5% and reduces classification time by up to 50% compared to the standard bag-of-features model.
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NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGESijcax
Semantic annotation of images is an important research topic on both image understanding and database
or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic
image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet
ontology) for the semantic relationships between tags in the classification and improving semantic gap
exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy
compared to the another methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGESijcax
This document summarizes a research paper that proposes a new ontology-based method for automatic image retrieval and annotation using 5,000 images from the Corel dataset. The method combines global and regional visual features with contextual relationships defined in an ontology. It creates a new ontology based on WordNet to semantically relate tags and reduce gaps between low-level features and high-level concepts. Experimental results show the proposed method increases annotation accuracy compared to other methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGESijcax
This document summarizes a research paper that proposes a new ontology-based method for automatic image retrieval and annotation using 5,000 images from the Corel dataset. The method combines global and regional visual features with contextual relationships defined in an ontology. It creates a new ontology based on WordNet to semantically relate tags and reduce gaps between low-level features and high-level concepts. Experimental results show the proposed method increases annotation accuracy compared to other methods.
Similar to An attribute assisted reranking model for web image search (20)
This document proposes a reduced latency list decoding algorithm and high throughput decoder architecture for polar codes. The reduced latency list decoding algorithm visits fewer nodes in the decoding tree and considers fewer possibilities of information bits than existing successive cancellation list decoding algorithms, significantly reducing decoding latency and improving throughput with little performance degradation. An implementation of the proposed decoder architecture in a 90nm CMOS technology demonstrates significant latency reduction and area efficiency improvement compared to other list polar decoders.
A researcher developed a radix-8 divider for binary64 division units to improve energy efficiency at high clock rates. Simulation results showed the radix-8 divider requires less energy per division than radix-4 or radix-16 approaches. The researcher used Xilinx 10.1 and ModelSim 6.4b tools and VHDL/Verilog languages to develop and test the minimally redundant radix-8 divider.
Hybrid FPGA architectures containing a mixture of lookup tables (LUTs) and hardened multiplexers are evaluated to improve logic density and reduce chip area. Simulation results show that non-fracturable hybrid architectures naturally save up to 8% area after placement and routing without optimizing the technology mapper, and additional area is saved with architecture-aware mapping optimizations. Fracturable hybrid architectures only provide marginal area gains of up to 2% after placement and routing. For the most area-efficient hybrid architectures, timing performance is minimally impacted.
Input-Based Dynamic Reconfiguration of Approximate Arithmetic Units for Video...Pvrtechnologies Nellore
This document proposes a reconfigurable approximate architecture for MPEG encoders that optimizes power consumption while maintaining a particular Peak Signal-to-Noise Ratio threshold for any input video. It designs reconfigurable adder/subtractor blocks that can modulate their degree of approximation, and integrates them into the motion estimation and discrete cosine transform modules of the MPEG encoder. Experimental results show the approach dynamically adjusts the degree of hardware approximation based on the input video to respect the given quality bound across different videos while achieving up to a 38% power saving over a conventional non-approximated MPEG encoder architecture.
This document lists 69 MATLAB projects related to various domains including image processing, biometrics, medical image processing, computer vision, and more. It provides the project titles, domains or sub-domains, programming languages and years. It also lists the support that will be provided to registered students including the IEEE base paper, documentation, source code, execution help, and publication support. The projects involve techniques such as image segmentation, super resolution, steganography, action recognition, and license plate detection. Support includes documentation templates, software installation guides, and assistance with conferences or journal publications.
This document lists 50 VLSI projects from 2016-2017 across various domains such as low power, high speed data transmission, area efficient/timing and delay reduction, audio/video processing, networking, verification, and Tanner/Microwind. The projects cover a range of topics including ECG acquisition systems, modular multiplication, adaptive radios, arrhythmia prediction, electrical capacitance tomography, approximate computing using memoization, FFT processors, error correction coding, carry skip adders, dynamic voltage and frequency scaling, code compression, turbo decoding, variable digital filters, parallel FFTs, MIMO communications, timing error correction, LU decomposition, FPGA logic architectures, LDPC decoding, minimum energy systems, elliptic curve multiplication, NB
This document lists 97 potential final year projects for students in various domains including embedded systems, Internet of Things, robotics, ZigBee, GSM and GPS, consumer electronics, automation, and more. It provides contact information for the project coordinator and describes the domain and programming language/year for each potential project. Students who register will receive support including an IEEE base paper, documentation, source code, and assistance with publication.
This document describes a high-speed FPGA implementation of an elliptic curve cryptography processor based on redundant signed digit representation. The processor employs pipelining techniques to achieve high throughput for Karatsuba–Ofman multiplication. It also includes an efficient modular adder without comparison and a high throughput modular divider to maximize frequency. The processor supports the NIST P256 curve and performs single-point multiplication in 2.26 ms at a maximum frequency of 160 MHz on a Xilinx Virtex 5 FPGA.
Retiming of digital circuits is conventionally based on the estimates of propagation delays across different paths in the data-flow graphs (DFGs) obtained by discrete component
timing model, which implicitly assumes that operation of a node can begin only after the completion of the operation(s) of its preceding node(s) to obey the data dependence requirement. Such a discrete component timing model very often gives much higher
estimates of the propagation delays than the actuals particularly when the computations in the DFG nodes correspond to fixed point arithmetic operations like additions and multiplications
Pre encoded multipliers based on non-redundant radix-4 signed-digit encodingPvrtechnologies Nellore
This paper introduces an architecture for pre-encoded multipliers used in digital signal processing based on offline encoding of coefficients using a non-redundant radix-4 signed-digit encoding technique. Experimental analysis shows the proposed multipliers using this encoding technique, along with a coefficients memory, are more efficient in terms of area and power compared to conventional Modified Booth encoding schemes.
Quality of-protection-driven data forwarding for intermittently connected wir...Pvrtechnologies Nellore
The document proposes a quality-of-protection (QoP)-driven data forwarding strategy for intermittent wireless networks. The existing system relies on collaborative data delivery, but non-cooperative behavior impairs network QoP and user quality of experience (QoE). The proposed system evaluates process-based and relationship-based credibility of nodes in a distributed manner using locally recorded forwarding behavior information. An intrusion detection mechanism helps select reliable relay nodes for transmission, improving QoP, QoE, reducing network load, and enhancing resource utilization. Numerical results show the proposed approach provides reliable data transmission with high QoP and improved user QoE.
The document provides information about an IT services company called Coalesce Technologies. It discusses Coalesce's services, commitment to client satisfaction, growing network, and customized solutions. It also describes the library management system project, including the problems with existing systems, proposed new system features, and UML diagrams for modeling the system. Key aspects of the proposed system include automating transactions, providing a simple GUI, efficient database updating, and restricting administrative access for security.
The document discusses an e-voting system project that aims to provide a secure and user-friendly online voting system. It outlines the existing paper-based voting system and proposes an online voting system where voters can cast their votes from anywhere in India through a database that stores voter information and votes. The proposed system is designed to accurately record and retrieve voter information and votes in a planned, reliable, and redundant manner. It requires hardware like a PC and Windows OS along with Java programming language to develop the online voting software.
PVR Technology lists 26 new web-based projects including an airline automation system, attendance management system, automated college admission system, clustering datasets using machine learning algorithms, corporate transportation system, courier information system, e-voting system, e-complaints system in PHP, e-learning system in PHP, exam result application, hairstylesalon system, inventory management system, library management system, medical reference system, online placement and training system, online admission system, online bank system, vehicle investigation system, resource management system in PHP, online vegetables purchase system, online shopping for gadgets system, online seat booking system, online requitment system, online quiz system, online placement and training cell system, online movie ticket system,
The document proposes a new medium access control (MAC) protocol called PoCMAC that uses distributed power control to manage interference in full-duplex WiFi networks. PoCMAC allows simultaneous uplink and downlink transmissions between an access point and half-duplex clients. It identifies regimes where power control provides throughput gains and develops a full 802.11-based protocol for distributed selection of three-node topologies. Simulations and software-defined radio experiments show PoCMAC achieves higher capacity and throughput compared to half-duplex networks, while maintaining similar fairness.
This document contains information about PVR Technology, including their head office location, website, email, and phone number. It then lists 20 project titles related to power electronics and renewable energy systems. The projects cover topics like power quality improvement, single-stage converters, grid-connected inverters, maximum power point tracking, stand-alone energy sources, power factor correction, resonant inverters, DC-DC converters, AC-DC converters, filters, and induction heating applications.
Control cloud-data-access-privilege-and-anonymity-with-fully-anonymous-attrib...Pvrtechnologies Nellore
This document proposes a scheme called AnonyControl to address privacy concerns with cloud data access. It aims to control access privileges while preserving user identity privacy. AnonyControl decentralizes the central authority across multiple attribute authorities to limit identity leakage and achieve semi-anonymity. It also generalizes file access control to privilege control to manage cloud data operations in a fine-grained way. The document describes existing access control schemes, disadvantages related to identity privacy, and how AnonyControl improves on previous work by protecting user identities through attribute distribution across authorities.
Control cloud data access privilege and anonymity with fully anonymous attrib...Pvrtechnologies Nellore
This document proposes two schemes, AnonyControl and AnonyControl-F, to address privacy issues in existing access control schemes for cloud storage. AnonyControl decentralizes the central authority to limit identity leakage and achieve semi-anonymity. It also generalizes file access control to privilege control. AnonyControl-F fully prevents identity leakage and achieves full anonymity. The schemes use attribute-based encryption and a multi-authority system to securely control user access privileges without revealing identity information. Security analysis shows the schemes are secure under certain cryptographic assumptions.
The document proposes CloudKeyBank, a key management framework that addresses the confidentiality, search privacy, and owner authorization of outsourced encryption keys. It does so using a new cryptographic primitive called Searchable Conditional Proxy Re-Encryption (SC-PRE) that combines Hidden Vector Encryption and Proxy Re-Encryption. The framework allows key owners to encrypt keys for outsourcing while maintaining privacy and granting controlled authorization. It aims to solve security issues not addressed by traditional outsourced data solutions.
Circuit ciphertext policy attribute-based hybrid encryption with verifiablePvrtechnologies Nellore
The document proposes a scheme for circuit ciphertext-policy attribute-based hybrid encryption with verifiable delegation in cloud computing. It aims to ensure data confidentiality, fine-grained access control, and verifiability of delegated computation results. The scheme uses a combination of ciphertext-policy attribute-based encryption, symmetric encryption, and verifiable computation. It is proven secure based on computational assumptions and simulations show it is practical for cloud computing applications.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
2. 262 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 1, JANUARY 2015
Fig. 1. Flowchart of the proposed attribute-assisted hypergraph reranking method. The search engine returns the images related to the textual query “baby”
and then our proposed approach is applied to reorder the result with attribute feature. We show the top-9 ranked images in the text-based search results and
the reranked results in the first and last block, respectively.
performance in assisting the task of image classification.
Besides that, an attribute is potentially any visual property that
humans can precisely communicate or understand, even if it
does not correspond to a traditionally-defined object part. For
instance, “red-dot in center of wings” is a valid attribute, even
though there is not a single butterfly part that corresponds to it.
Furthermore, the type of the most effective features should
vary across queries. For example, for queries that are related
to color distribution, such as sunset, sunrise and beach, color
features will be useful. For some queries like building and
street, edge and texture features will be more effective. It can
be understood that semantic attribute could also be viewed
a description or modality of image data. Using multimodal
features can guarantee that the useful features for different
queries are contained. Therefore, all these superiorities drive
us to exploit semantic attributes for image representation in
the task of web image search reranking.
Motivated by the above observations, we move one step
ahead of visual reranking and propose an attribute-assisted
reranking approach. Fig. 1 illustrates the flowchart of our
proposed method. After a query “baby” is submitted, an
initial result is obtained via a text-based search engine.
It is observed that text-based search often returns “incon-
sistent” results. Some visually similar images are scattered
in the result while other irrelevant results are filled between
them, such as “dog” and “disney baby”. Based on the
returned images, both visual features and attribute features
are extracted. In particular, the attribute feature of each
image consists of the responses from the binary classifiers
for all the attributes. These classifiers are learned offline.
Visual representation and semantic description are simul-
taneously exploited in a unified model called hypergraph.
The preliminary version of this work, which integrates attribute
feature and visual feature to improve the reranking perfor-
mance is published in [28]. In this paper, we extend this
work to analyze semantic attributes characteristic and discover
that only limited attributes distributed in each image. Hence
we propose that the selection of attribute features could be
conducted simultaneously through the process of hypergraph
learning such that the effects of semantic attributes could
be further tapped and incorporated in the reranking frame-
work. Compared with the previous method, a hypergraph is
reconstructed to model the relationship of all the images,
in which each vertex denotes an image and a hyperedge
represents an attribute and a hyperedge connects to multiple
vertices. We define the weight of each edge based on the
visual and attribute similarities of images which belongs to
the edge. The relevance scores of images are learned based
on the hypergraph. The advantage of hypergraph can be
summarized that not only does it take into account pair-
wise relationship between two vertices, but also higher order
relationship among three or more vertices containing grouping
information. Essentially, modeling relationship among more
close samples will be able to preserve the stronger semantic
similarity and thus facilitate ranking performance.
However, even these merits,we have to face the challenges
it brings to this unified formulation at the same time: 1) simply
learning classifiers by fitting them to all visual features often
fails to generalize the semantics of the attributes correctly
because low-level features are extracted by region or interest
point detector instead of aiming to depict specific attribute.
So we need to select representative features which are in favor
to describe current semantic attributes. We propose to take
advantage of 1 regularized logistic regression trained for each
attribute within each class. 2) as attribute features are formed
by prediction of several classifiers, semantic description of
each image might be inaccurate and noisy. Hence we propose
a regularizer on the hyperedge weights which performs a
weighting or selection on the hyperedges. In this way, for
attributes or hyperedges that are informative, higher weights
will be assigned. In contrast, noisy hyperedges will be implicit
removed when the weights converges to zeros after hypergraph
learning. Finally, we can obtain the reranked list of the
images with respect to relevance scores in descending order.
We conduct experiments on MSRA-MM V2.0 dataset [7]. The
experimental results demonstrate superiority of the proposed
attribute-assisted reranking approach over other state-of-the-art
reranking methods and their attribute-assisted variants.
The main contribution of this paper can be summarized as
follows:
• We propose a new attribute-assisted reranking method
based on hypergraph learning. We first train several
classifiers for all the pre-defined attributes and each
image is represented by attribute feature consisting of
the responses from these classifiers. Different from the
existing methods, a hypergraph is then used to model
the relationship between images by integrating low-level
features and attribute features.
• We improve the hypergraph learning method approach
presented in [28] by adding a regularizer on the hyperedge
weights which performs an implicit selection on the
semantic attributes. This makes our approach much more
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3. CAI et al.: ATTRIBUTE-ASSISTED RERANKING MODEL FOR WEB IMAGE SEARCH 263
robust and discriminative for image representation as
noisy attributes will be removed and informative ones
will be selected.
• We conduct comprehensive experiments to empirically
analyze our method on more than 1,000 queries and
1 million images. The experimental results validate the
effectiveness of our method.
The rest of the paper is organized as follows. Firstly,
we provide a review of the related work on Web image
search reranking, semantic attribute and hypergraph learning
in Section II. The proposed attribute-assisted reranking
approach is elaborated in Section III. Specifically, we elab-
orate the image features and attribute learning methods
in Section III-A and Section III-B, respectively. The proposed
hypergraph construction strategy is detailed in Section III-C.
In Section IV, we report our experimental results on a Web
image dataset: MSRA-MM V2.0 dataset, followed by the
conclusions in Section V.
II. RELATED WORK
In this section, we provide a brief description of the existing
visual search reranking approaches, review the semantic
attributes exploited in recent literature, and describe the hyper-
graph learning theory.
A. Web Image Search Reranking
Web image search reranking is emerging as one of the
promising techniques for automative boosting of retrieval
precision [2]. The basic functionality is to reorder the retrieved
multimedia entities to achieve the optimal rank list by exploit-
ing visual content in a second step. In particular, given a
textual query, an initial list of multimedia entities is returned
using the text-based retrieval scheme. Subsequently, the most
relevant results are moved to the top of the result list while
the less relevant ones are reordered to the lower ranks. As
such, the overall search precision at the top ranks can be
enhanced dramatically. According to the statistical analysis
model used, the existing reranking approaches can roughly
be categorized into three categories including the clustering
based, classification based and graph based methods.
1) Clustering-Based Methods: Clustering analysis is very
useful to estimate the inter-entity similarity. One good example
of clustering based reranking algorithms is the Information
Bottle based scheme developed by Hsu et al. [9]. In this
method, the images in the initial results are primarily grouped
automatically into several clusters. Then the re-ranked result
list is created first by ordering the clusters according to
the cluster conditional probability and next by ordering the
samples within a cluster based on their cluster membership
value. In [18], a fast and accurate scheme is proposed for
grouping Web image search results into semantic clusters.
It is obvious that the clustering based reranking methods can
work well when the initial search results contain many near-
duplicate media documents. However, for queries that return
highly diverse results or without clear visual patterns, the
performance is not guaranteed.
2) Classification-Based Methods: In the classification based
methods, visual reranking is formulated as binary classification
problem aiming to identify whether each search result is
relevant or not. For instance, a classifier or a ranking model
is learned with the pseudo relevance feedback (PRF) [12].
However, in many real scenarios, training examples obtained
via PRF are very noisy and might not be adequate for training
effective classifier. To address this issue, Schroff et al. [3]
learned a query independent text based re-ranker. The top
ranked results from the text based reranking are then selected
as positive training examples. Negative training examples are
picked randomly from the other queries. A binary SVM
classifier is then used to re-rank the results on the basis of
visual features.
3) Graph-Based Methods: Graph based methods have been
proposed recently and received increased attention as demon-
strated to be effective. Jing and Baluja proposed a VisualRank
framework to efficiently model similarity of Google image
search results with graph [19]. The framework casts the rerank-
ing problem as random walk on an affinity graph and reorders
images according to the visual similarities. The final result
list is generated via sorting the images based on graph nodes’
weights. In [2], Tian et al. presented a Bayesian reranking
framework formulating the reranking process as an energy
minimization problem. The objective is to optimize the con-
sistency of ranking scores over visually s5imilar samples and
minimize the inconsistency between the optimal list and the
initial list. Thus, the performance is significantly dependent on
the statistical properties of top ranked search results. Motivated
by this observation. Wang et al. proposed a semi-supervised
framework to refine the text based image retrieval results via
leveraging the data distribution and the partial supervision
information obtained from the top ranked images [20].
B. Semantic Attributes
Semantic attributes can be regarded as a set of mid-level
semantic preserving concepts. Different from low-level visual
features, each attribute has an explicit semantic meaning,
e.g., “animals”. Attribute concepts also differ from specific
semantics since they are relatively more general and easier
to model, e.g., attributes “animal” and “car” are easier to
model and distinguish than the concrete semantic concepts
“Husky” and “Gray Wolves”. Due to the advantages of
being semantic-aware and easier to model, attributes have
been studied recently and are revealing their power in var-
ious applications such as object recognition [5], [6], [11],
and image/video search [4]. Thus, attributes are expected
to narrow down the semantic gap between low-level visual
features and high-level semantic meanings. By using attribute
classifiers, Su et al. [27] propose to alleviate the semantic
gap between visual words and high level concept, focus-
ing on polysemy phenomenon of particular visual words.
By randomly splitting the training data, Farhadi et al. [5]
exhaustively train thousands of classifiers and then chose
some of the discriminative ones as attributes (e.g., attributes
that “cat” and “dog” have but “sheep” and “horse” do not).
Kumar et al. [6] define a set of binary attributes called
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TABLE I
NOTATIONS AND DEFINITIONS
similes for face verifications. Each attribute detector in smiles
are exclusively trained for one specific category, e.g., “the
Angelina Jolie’s mouth”. However, such category-specific
attribute detectors are contrary to the spirit of attributes,
e.g., concept that is generalizable and transferrable. Recently,
Parikh and Grauman [17] propose a new strategy to compare
the relative strength of attributes, e.g., “while A and B are both
shiny, A is shinier than B”. Instead of being trained by binary
classifiers, relative attributes are learned by ranking functions
(e.g., the ranking SVM). The output of a ranking function
indicates the relative presence of the corresponding attribute.
In addition, there are works aiming to discover attributes
either automatically through the web or semi-automatically
with human in the loop [16], and to explore new types of
attributes [17]. To date, they explore active learning strate-
gies for training relative attribute ranking functions, such as
Learning to rank model to achieve relative attributes [29].
Zhang et al. [30] proposed an attribute-augmented semantic
hierarchy for content-based image retrieval. They employ
attributes to describe the multiple facts of the concept and
hybrid feedbacks of attributes and images are also collected.
Such superiority motivates us to exploit attributes for visual
reranking.
C. Hypergraph Learning
Before presenting our approach, we first briefly introduce
the hypergraph learning theory.
In a simple graph, samples are represented by vertices and
an edge links the two related vertices. Learning tasks can
be performed on a simple graph. Assuming that samples are
represented by feature vectors in a feature space, an undirected
graph can be constructed by using their pairwise distances,
and graph-based semi-supervised learning approaches can be
performed on this graph to categorize objects. It is noted
that this simple graph cannot reflect higher-order information.
Compared with the edge of a simple graph, a hyperedge in
a hypergraph is able to link more than two vertices. For
clarity, we first illustrate several important notations and their
definitions throughout the paper in Table 1.
A hypergraph G = (V, E, w) is composed by a vertex
set V , an edge set E, and the weights of the edges w. Each
edge e is given a weight w(e). The hypergraph G can be
denoted by a | V | × | E | incidence matrix H with entries
defined as:
h(vi , ej ) =
1 i f vi ∈ ej ,
0 otherwise
(1)
For a vertex v ∈ V , its vertex degree can be estimated by:
d(v) =
e∈E
w(e)h(v, e) (2)
For a hyperedge e ∈ E, its hyperedge degree can be
estimated by:
δ(e) =
v∈V
h(v, e) (3)
We use Dv and De to denote the diagonal matrices
containing the vertex and hyperedge degrees respectively, and
let W denote the diagonal matrix containing the weights of
hyperedges.
W(i, j) =
w(i) i f i = j
0 otherwise
(4)
In a hypergraph, many machine learning tasks can be
performed, i.e. clustering [15] and classification [13]. The
binary classification is taken as an example here. For hyper-
graph learning, the Normalized Laplacian method proposed
in [15] is employed, and it is formulated as a regularization
framework:
arg min
f
{λRemp( f ) + ( f )} (5)
where f is the relevance score to be learned, ( f ) is the
normalized cost function, Remp( f ) is empirical loss, and λ is
a weighting parameter. By minimizing this cost function,
vertices sharing many incidental hyperedges are guaranteed
to obtain similar relevance scores. The regularizer on the
hypergraph is defined as:
( f ) =
1
2
e∈E u,v∈e
w(e)h(u, e)h(v, e)
δ(e)
f (u)
√
d(u)
−
f (v)
√
d(v)
2
(6)
Let = D
−
1
2
v H W D−1
e HT D
−
1
2
v , and = I − . Here the
normalized cost function can be rewritten as:
( f ) = f T
f (7)
Here is a positive semi-definite matrix called hypergraph
Laplacian.
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III. ATTRIBUTE-ASSISTED IMAGE
SEARCH RERANKING
In this section, we elaborate the proposed attribute-assisted
image search reranking framework. We elaborate image
features in Section III-A, and then introduce the proposed
attribute learning method in Section III-B. Finally, we describe
our hypergraph construction algorithm in Section III-C.
A. Image Features
We used four types of features, including color and texture,
which are good for material attributes; edge, which is use-
ful for shape attributes; and scale-invariant feature transform
(SIFT) descriptor, which is useful for part attributes. We used a
bag-of-words style feature for each of these four feature types.
Color descriptors were densely extracted for each pixel as
the 3-channel LAB values. We performed K-means clustering
with 128 clusters. The color descriptors of each image were
then quantized into a 128-bin histogram. Texture descriptors
were computed for each pixel as the 48-dimensional responses
of texton filter banks. The texture descriptors of each image
were then quantized into a 256-bin histogram. Edges were
found using a standard canny edge detector and their orienta-
tions were quantized into 8 unsigned bins. This gives rise to a
8-bin edge histogram for each image. SIFT descriptors were
densely extracted from the 8 × 8 neighboring block of each
pixel with 4 pixel step size. The descriptors were quantized
into a 1,000-dimensional bag-of-words feature.
Since semantic attributes usually appear in one or more
certain regions in an image, we further split each image
into 2 × 3 grids and extracted the above four kinds of
features from each grid respectively. Finally, we obtained a
9,744-dimensional feature for each image, consisting of
a 1, 392 × 6-dimensional feature from the grids and a
1,392-dimensional feature from the image. This feature was
then used for learning attribute classifiers.
B. Attribute Learning
We learn a Support Vector Machine (SVM)1 classifier for
each attribute. However, simply learning classifiers by fitting
them to all visual features often fails to generalize the seman-
tics of the attributes correctly. For each attribute, we need to
select the features that are most effective in modeling this
attribute. It is necessary to conduct this selection based on the
following two observations: 1) such a wealth of low level fea-
tures are extracted by region or interest point detector, which
means these extraction may not aim to depict the specific
attribute and include redundant information. Hence we need
select representative and discriminative features which are in
favor to describe current semantic attributes. 2) the process
of selecting a subset of relevant features has been playing an
important role in speeding up the learning process and allevi-
ating the effect of the curse of dimensionality. We here apply
the feature selection method as described in [5]. In particular,
if we want to learn a “wheel” classifier, we select features
that perform well at distinguishing examples of cars with
1http://www.csie.ntu.edu.tw/cjlin/libsvm
“wheels” and cars without “wheels”. By doing so, we help
the classifier avoid being confused about “metallic”, as both
types of example for this “wheel” classifier have “metallic”
surfaces. We select the features using an 1-regularized logistic
regression trained for each attribute within each class, then
pool examples over all classes and train using the selected
features. Such regression model is utilized as the preliminary
classifiers to learn sparse parameters. The features are then
selected by pooling the union of indices of the sparse non-
zeros entries in those parameters. The regularization parame-
ters of 1-norm regression was set to 0.01 empirically and
the parameters of SVM classifiers were determined by five-
fold cross validation. For example, we first select features that
are good at distinguishing cars with and without “wheel” by
fitting an 1-regularized logistic regression to those examples.
We then use the same procedure to select features that are
good at separating motorbikes with and without wheels, buses
with and without wheels, and trains with and without wheels.
We then pool all those selected features and learn the “wheel”
classifier over all classes using those selected features. In this
way, we select effective features for each attribute and the
selected features are then used for learning the SVM classifier.
C. Attribute-Assisted Hypergraph Construction
We propose an attribute-assisted hypergraph learning
method to reorder the ranked images which returned from
search engine based on textual query. Different from the typi-
cal hypergraph [10], [25], [26], it presents not only whether a
vertex v belongs to a hyperedge e, but also the prediction score
that v is affiliated to a specific e. The weight is incorporated
into graph construction as tradeoff parameters among various
features. Our modified hypergraph is thus able to improve
reranking performance by mining visual feature as well as
attribute information.
The hypergraph model has been widely used to exploit
the correlation information among images. In this paper,
we regard each image in the data set as a vertex on hypergraph
G = (V, E, w). Assume there are n images in the data set, and
thus, the generated hypergraph contains n vertices. Let V =
{v1, v2, . . . , vn} denote n vertices and E = {e1, e2, . . . , em}
represent m hyperedges where the images sharing the same
attribute are connected by one hyperedge. For various hyper-
edges, we set the weight vector to be w = [w1, w2, ..., wm]
in the hypergraph, where
m
i=1
wi = 1. In each hyperedge,
we select K images which offer more preference to corre-
sponding attribute based on the descending order of classifier
scores. So, the size of a hyperedge in our framework is K. The
incidence matrix H of a hypergraph is defined as follows:
h(vi , ej ) =
svi i f vi ∈ ej ,
0 otherwise
(8)
where svi represents the j-attribute classifier score of image vi .
Then for a vertex v ∈ V, we define vertex degree to be
d(v) = e∈E w(e)h(v, e). For a hyperedge e ∈ E, its degree
is defined as δ(e) = v∈V h(v, e). We use Dv and De
to denote the diagonal matrices containing the vertex and
hyperedge degrees respectively, and let W denote the diagonal
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matrix containing the weights of hyperedges. For the weight
initialization, we define the similarity matrix A between two
images as follows:
A(i, j)
= γ1 Aattribute + γ2 Alocal + γ3 Aglobal
= γ1 exp −
Disattribute(i, j)
¯Dattribute
+ γ2 exp −
Dislocal(i, j)
¯Dlocal
+ γ3 exp −
3
k=1
Disgk(i, j)
¯Dgk
(9)
where Disg(i, j), Dislocal(i, j) and Disattribute(i, j) are pair-
wise Euclidean distance between vi and v j of global feature,
local feature and attribute feature, respectively. For instance,
Disattribute(i, j) can be denoted as:
Disattribute(i, j) = ia − ja 2
= (ia − ja)T
(ia − ja) (10)
ia and ja indicate semantic attribute features of two images.
After all the distance matrices for three kinds of features are
obtained, the similarity matrix A between two images can be
obtained and γ1, γ2, γ3 are normalizing constants to keep the
proportion of various features to be 1. And ¯D is the mean
value of elements in the distance matrix. The initial weight
w(ei ) is set to w(ei ) = v j ∈ei
A(i, j).
For hypergraph learning, it is formulated as a regularization
framework:
arg min
f,w
{λRemp( f ) + ( f ) + μ (w)} (11)
where f is the relevance score to be learned, ( f ) is the
normalized cost function, Remp( f ) is empirical loss and (w)
is a regularizer on the weights. Instead of fixing hyperedge
weights, we assume that they have Gaussian distribution, such
that the weights w can be learned together with the relevance
score f .
By minimizing this cost function, vertices sharing many
incidental hyperedges are guaranteed to obtain similar rele-
vance scores. Defining = D
−
1
2
v H W D−1
e HT D
−
1
2
v , we can
derive the cost function as
( f )
=
1
2
e∈E u,v∈e
w(e)h(u, e)h(v, e)
δ(e)
f (u)
√
d(u)
−
f (v)
√
d(v)
2
=
1
2
e∈E u,v∈e
w(e)h(u, e)h(v, e)
δ(e)
f 2(u)
√
d(u)
−
f (u) f (v)
√
d(u)d(v)
=
u∈V
f 2
(u)
e∈E
w(e)h(u, e)
d(u)
v∈V
h(v, e)
δ(e)
−
e∈E u,v∈e
f (u)h(u, e)w(e)h(v, e) f (v)
√
d(u)d(v)δ(e)
= f T
(I − θ) f
= f T
f (12)
where I is the identity matrix, = I − is a positive
semi-definite matrix called hypergraph Laplacian. To force the
assigned relevance score to approach the initial relevance score
of y, a regularization term is defined as follows:
Remp( f ) = f − y 2
=
u∈V
( f (u) − y(u))2
(13)
In the constructed hypergraph, the hyperedges are with
different effects as there exists a lot of uninformative attributes
for a given query. Therefore, performing a weighting or
selection on the hyperedges will be helpful. Here we integrate
the learning of the hyperedge weights into the formulation.
(w) = w q (14)
Different from the conventional hypergraph learning
described in Section II-C, we add a 2 norm regularizer
on the weights w 2
2
. This strategy is popular to avoid
overfitting [13]. The regularization framework then becomes
arg min
f,w
( f ) = arg min
f,w
{ f T
f + λ f − y 2
+ μ
m
i=1
wi
2
}
(15)
where λ, μ are the regularization parameters. Although the
function is not jointly convex with respect to f and w, the
function is convex with respect to f with fixed w or vice versa.
Thus, we can use the alternating optimization to efficiently
solve the problem.
We first fix w by differentiating ( f ) with respect to f
and we have:
∂ ( f )
∂ f
= f + λ( f − y) = 0 (16)
Following some simple algebraic steps, we have
f =
λ
1 + λ
(I −
1
1 + λ
)−1
y (17)
We define α = 1/(1 + λ). Noticing that λ/(1 + λ) is a
constant and does not change the ranking results, we can
rewrite f as follows:
f = (1 − α)(I − α )−1
y (18)
Note that, the matrix I − α is highly sparse. Therefore, the
computation can be very efficient. We then fix f and minimize
with respect to w,
arg min
w
( f ) = arg min
w
{ f T
f + μ
m
i=1
wi
2
}
s.t.
m
i=1
wi = 1, μ > 0 (19)
We adopt alternating optimization to solve the above
problem. Each time we only update one set of variables
(we have m + 2 sets of variables in all). By using a Lagrange
multiplier η to take the constraint
m
i=1
wi = 1 into considera-
tion, we get the objective function:
arg min
w,η
( f )=arg min
w,η
{ f T
f + μ
m
i=1
wi
2
+ η(
m
i=1
wi − 1)}
(20)
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Algorithm 1 Attribute-Assisted Hypergraph Learning
By setting the derivative of ( f ) with respect to wi,
we can obtain,
∂ ( f )
∂wi
=
∂
∂wi
f T
(I − D
−
1
2
v H W D−1
e HT
D
−
1
2
v ) f + μ
m
i=1
w2
i
+ η(
m
i=1
wi − 1)
= − f T
D
−
1
2
v H D−1
e HT
D
−
1
2
v f +2mμ+mη=0 (21)
Let = D
−
1
2
v H, we have
η =
f T D−1
e
T f − 2μ
m
(22)
and
wi = −
− f T D−1
e
T f + η
2μ
= −
−m f T D−1
e
T f + f T D−1
e
T f − 2μ
2μm
(23)
In the aforementioned alternating optimization process, since
each step decreases the objective function, which has a lower
bound 0, the convergence of the alternating optimization is
guaranteed. Algorithm 1 summarizes the implementation of
the alternating optimization.
D. Utilization of Text-Based Search Prior
Since in reranking, the text-based search provides original
ranking lists instead of quantized scores, a necessary step is
to turn the ranking positions into scores. Traditional methods
usually associate yi with the position i using heuristic strate-
gies, such as normalized rank yi = 1−i/N or rank yi = N−i.
In this work, we investigate the relationship between yi and
the position i with a large number of queries. Actually, we
can define
yi = Eq∈Q[q, i] (24)
Fig. 2. The average relevance scores at different ranking positions and the
fitted curve [26].
where Q means the set of all possible queries, Eq∈Q means
the expectation over the query set Q, and y(q, i) indi-
cates the relevance ground truth of the i-th search result
for query q. Therefore, the most intuitive approach is to
estimate yi by averaging y(q, i) over a large query set.
Fig. 2 illustrates the results obtained by using more than
1,000 queries. Details about the queries and the dataset will
be introduced in Section IV. However, as shown in Fig. 2,
the average relevance score curve with respect to the ranking
position is not smooth enough even after using more than
1,000 queries. A prior knowledge can be that the expected
relevance score should be decreasing with respect to ranking
position. Therefore, we further smooth the curve with a
parametric approach. We assume y = a + be−i/c and then
fit this function with the non-smooth curve. In this way, with
mean squared loss criterion we estimate the parameters a, b
and c to be 1, 0.4, 141, respectively. Fig. 2 shows the fitted
curve, and we can see that it reasonably preserves the original
information.
IV. EXPERIMENTS
In this section we first describe the experimental dataset
that we used to evaluate our proposed approach. Then we
present the results of evaluation at different levels and verify
the effectiveness of our method.
A. Dataset
We use the MSRA-MM V2.0 dataset as our experimental
data. This dataset consists of about 1 million images from
1, 097 diverse yet representative queries collected from the
query log of Bing [7]. We choose this dataset to evaluate our
approach for the following reasons: (1) it is a real-world web
image dataset; (2) it contains the original ranking information
of a popular search engine, and thus we can easily evaluate
whether our approach can improve the performance of the
search engine; (3) it is publicly available. There are roughly
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TABLE II
THE STATISTICAL INFORMATION ABOUT IMAGE DATASET
Fig. 3. Several example images with different relevance levels with respect
to “Steve Jobs”, “Airplane”, “Cattle”.
900 images for each query. For each image, its relevance
to the corresponding query is labeled with three levels: very
relevant, relevant and irrelevant. These three levels are indi-
cated by scores 2, 1 and 0, respectively. Table II shows the
number of queries at various categories and Fig. 3 illustrates
several example images of “Steve Jobs”,“Airplane”,“Cattle”
with different relevance levels. The queries are used for
reranking performance evaluation. We first randomly select
40 queries from two categories “animal” and “object”. All
the very relevant images within these queries are kept for
attribute classifiers training. We defined 108 attributes by
referring to the attributes in [14] as listed in Table III, such as
“Beak” ,“Engine”. The groundtruth of attributes are manually
annotated. As aforementioned in Section III-A, there are four
types of features extracted including color, texture, edge and
SIFT descriptor. We adopt a bag-of-words style for each of
these four features and obtained 9,744-dimensional feature for
each image.
B. Evaluation Measure
We adopt Normalized Discounted Cumulative
Gain (NDCG) [8], which is a standard evaluation in
information retrieval when there are more than two relevance
levels, to measure the performance. Given a ranking list, the
NDCG score at position n is defined as,
N DCG@n = Zn
n
j=1
2r( j) − 1
log(1 + j)
(25)
where r( j) is the the relevance score of jth image in the
ranking list, Zn is the normalization factor which is chosen to
Fig. 4. Performance comparison between our method and conventional
methods on MSRA-MM dataset.
guarantee the perfect ranking list’s NDCG@n is 1, n is trun-
cation level. To evaluate the overall performance, we average
the NDCGs over all queries to obtain the Mean NDCG.
C. Experimental Results and Analysis
We first evaluate the effectiveness of our attribute classifiers
on the 1,097 testing queries. Regarding the outputs of each
attribute classifier on all the images, we use both binary classi-
fication decision and continuous confidence scores. The binary
outputs are used to evaluate the classifier performance and the
confidence score are used to calculate probabilistic hyperedge.
Due to the high cost of manual labeling, we only label the 108
attributes on the top 10 images from initial text search for each
query. We adopt the widely used metric AUC (area under
ROC curve) value for evaluating the accuracies of attribute
classifiers. The true positive (tp) and true negative (tn) are
divided by the total number of samples (n) and classification
accuracy is denoted as:
Accuracy =
tp + tn
n
(26)
Fig. 5 shows classification accuracy on each attribute. The
experimental results demonstrate that semantic description of
each image might be inaccurate and noisy, as the attribute
features are formed by prediction of several classifiers.
1) Performance Comparison Among Cluster-Based,
Classification-Based, Graph-Based: To verify the
effectiveness of the proposed attribute-assisted reranking
method, we compare the following approaches for
performance evaluation:
• Information Bottle [9]. The reranking approach applies
information bottleneck clustering over visual features
with the help of a smoothed initial ranking. The method
is denoted as “cluster-based”.
• Pseudo Relevance Feedback (PRF) [12]. Given a query,
we use top 100 search results in the original ranking list
as positive samples, and randomly collect 100 images
from the whole dataset and use them as negative samples.
We adopt RBF kernel based on these samples and learn
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TABLE III
108 SEMANTIC ATTRIBUTES USED IN THE MSRA-MM DATASET
Fig. 5. Attribute classification comparison on MSRA-MM dataset.
SVM classifier to rerank the search results. The approach
is denoted as “PRF-SVM”.
• Bayesian Reranking [2]. Since the Local-Pair variant
of Bayesian reranking [2] performs the best among the
six Bayesian variants reranking approaches, we will use
it as the representative of Bayesian reranking methods.
The method is denoted as “Pair-Local”.
We fixed the weights in Equation 9 as follows: γ1, γ2, γ3
all equal to 1/3 when attribute feature has been explored;
γ2 = γ3 = 1/2 and γ1 = 0 when independent visual feature
has been evaluated in the experiment.
The overall performance is shown in Fig. 4. From this
result, we can see that the proposed attribute-assisted rerank-
ing method consistently outperforms Bayesian reranking
at all truncation levels. Specifically, our method obtains
7.3% and 3.9% relative improvements on MNDCG@100 com-
pared with the Bayesian reranking. In addition, for attributed-
assisted Pair-Local method, it has relatively improved 1.8%
on MNDCG@40 in comparison with original reranking
approach. The main reason is that our method involves
beneficial attribute features throughout the reranking frame-
work. Additionally, we also explore and create visual
neighborhood relationship for each hyperedge instead of
isolated visual similarity mainly used in the Bayesian
reranking.
TABLE IV
PERFORMANCE COMPARISON FOR HYPERGRAPH RERANKING
2) Performance Comparison for Hypergraph Reranking:
In the above, we have verified that our proposed hypergraph
reranking approach performs the best in comparison with
the conventional reranking strategies. In this section, we will
further confirm the superiority of joint hypergraph learning
approach by adding a robust regularizer on hyperedge weights.
We denote the method published in preliminary work [28] as
“Hypergraph Reranking”, where we concatenate all features
into a long vector and construct hypergraph based on their
visual similarity. The performance in terms of MNDCG of
the two reranking methods as well as the text baseline are
illustrated in Table IV. We can see that the presented hyper-
graph learning approach assisted with 2 regularizer performs
better than Hypergraph Reranking. It achieves around 3.8%
improvements at MNDCG@20. Moreover, to demonstrate the
robustness of the proposed regularizer, we also conduct exper-
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Fig. 6. Performance comparison with various hyperedge size K.
iments with a 1 regularizer that encourages the sparsity in
the w 1
representation. From the experimental comparison,
we could see that our approach is more favorable in the task
of Web image search reranking, which improves the baseline
steadily and outperforms the other reranking strategies.
The good performance of the novel approach for Web image
search reranking could be attributed to the following appealing
properties: (1) since attribute features are formed by prediction
of several classifiers, semantic attributes description might be
noisy and only limited sematic attributes could be distributed
in a single image. Such implicit attribute selection could make
hypergraph based approach much more robust to improve
the reranking performance, as inaccurate attributes could be
removed and informative ones have been selected for image
representation. (2) our proposed iterative regularization frame-
work could further explore the semantic similarity between
images by aggregating their local, global similarities instead
of simple fusion with concatenation.
As the hyperedges in the graph are formed by K images
sharing the common semantic attribute, we perform evaluation
on reranking performance with various sizes of hyperedge K.
For each K value plotted, we perform the reranking com-
parison with attribute feature and without its help. Fig. 6
shows the comparison of mean NDCG@10. It achieves the
best performance when we set K as 40 among all selected
values. It seems that larger or smaller size of hyperedge may
result in lower reranking performance in terms of involving
more harmful or less useful attributes. We could also see that
our proposed reranking approach with local and global feature
is boosted by mining attribute information in the experiment.
We also evaluate two weighting parameters λ and μ in
our formulation. They modulate the effects of the loss term
f − y 2
and the regularizer term w 2
2
respectively. The
parameter λ is widely used in graph or hypergraph learning
algorithms, and its value determines the closeness of f and y.
For parameter μ, if its value tends to be zero, then the proposed
algorithm will degenerate to Equation 5 , i.e., our preliminary
proposed algorithm in [28]. If its value tends to be infinite,
then the optimal result is that only one weight is 1 and all
others are 0. This extreme case means that there will be only
one hyperedge weight used. In Fig. 7, we demonstrate the
Fig. 7. The average NDCG@10 performance with respect to the variation
of λ and μ.
Fig. 8. Some example images with semantic attributes of top 5 highest
weights.
mean NDCG@10 performance bar chart with respect to the
two parameters μ and λ. It can be seen that the mNDCG is
generally higher when we fix λ to be 20. The experimental
results illustrate that our approach is able to outperform the
other methods when the parameters vary in a wide range.
3) Performance Evaluation for Selected Semantic
Attributes: To further illustrate the importance role of
semantic attributes on the reranking framework, we conduct
experimental evaluation on the hyperedges that have the
highest weights. In Table III, it has been pointed out that
the top 20 attributes with higher weights in the proposed
approach and such semantic attributes are highlighted in
italic blue: “Face”, “Arm”, “Leg”, “Hand”, “Tail”, “Beak”,
“Wheel”, “Cloth”, “Furry”, “Nose”, “Wing”, “Wood”,
“Rock”, “Leaf”, “Headlight”, “Feather”, “Flower”, “Vehicle
Part”, “Dotted”, “Smooth”. We also illustrate the semantic
attributes that have top 5 highest weights on some images in
Fig. 8. We could see that beneficial attributes are distributed
in exemplar image which preserve the stronger sematic
similarity and thus facilitate ranking performance. Moreover,
it is also observed that the semantic attributes with a lower
classification score might give a low or high weight in the
hypergraph, in regardless of noisiness of predicted attributes.
Hence, the experimental results demonstrate the robustness
of our proposed approach.
Fig. 9 shows some visualized results of our Attribute-
assisted Hypergraph Reranking in comparison to Bayesian
reranking and Cluster-based approach on MSRA-MM dataset.
It is obvious that that our proposed approach significantly
outperforms the baselines methods.
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11. CAI et al.: ATTRIBUTE-ASSISTED RERANKING MODEL FOR WEB IMAGE SEARCH 271
Fig. 9. Case study of the visualized search examples between the proposed method (first row in each example) and baselines of Bayesian reranking [2]
(second row in each example) and Cluster-based approach [9] (third row in each example) on MSRA-MM dataset.
V. CONCLUSIONS
Image search reranking has been studied for several years
and various approaches have been developed recently to
boost the performance of text-based image search engine
for general queries. This paper serves as a first attempt to
include the attributes in reranking framework. We observe
that semantic attributes are expected to narrow down the
semantic gap between low-level visual features and high-
level semantic meanings. Motivated by that, we propose a
novel attribute-assisted retrieval model for reranking images.
Based on the classifiers for all the predefined attributes, each
image is represented by an attribute feature consisting of
the responses from these classifiers. A hypergraph is then
used to model the relationship between images by integrating
low-level visual features and semantic attribute features.
We perform hypergraph ranking to re-order the images,
which is also constructed to model the relationship of all
images. Its basic principle is that visually similar images
should have similar ranking scores and a visual-attribute
joint hypergraph learning approach has been proposed to
simultaneously explore two information sources. We conduct
extensive experiments on 1,000 queries in MSRA-MM V2.0
dataset. The experimental results demonstrate the effectiveness
of our proposed attribute-assisted Web image search reranking
method.
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Junjie Cai is currently pursuing the Ph.D. degree with the Department of
Computer Science, University of Texas at San Antonio, San Antonio, TX,
USA. His research interests include large-scale media search, social media
sharing and management, computer vision, and machine learning.
Zheng-Jun Zha (M’08) received the B.E. and Ph.D. degrees from the
Department of Automation, University of Science and Technology of China,
Hefei, China, in 2004 and 2009, respectively. He is currently a Professor with
the Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei.
His current research interests include multimedia content analysis, computer
vision, and multimedia applications, such as search, recommendation, and
social networking.
Meng Wang is currently a Professor with the Hefei University of Technology,
Hefei, China. He received the Ph.D. degree from the University of Science and
Technology of China, Hefei. His current research interests include multimedia
content analysis, search, mining, recommendation, and large-scale computing.
Shiliang Zhang is currently a Research Fellow with the Department of
Computer Science, University of Texas at San Antonio, San Antonio, TX,
USA. He was with Microsoft Research Asia, Beijing, China, as a Research
Intern, from 2008 to 2009. He returned to the Key Laboratory of Intelligent
Information Processing, Institute of Computing Technology, Chinese Acad-
emy of Sciences, Beijing, in 2009. His research interests include large-scale
image and video retrieval, image/video processing, and multimedia content
affective analysis.
Qi Tian (M’96–SM’03) received the B.E. degree in electronic engineering
from Tsinghua University, Beijing, China, in 1992, the M.S. degree in
electrical and computer engineering from Drexel University, Philadelphia, PA,
USA, in 1996, and the Ph.D. degree in electrical and computer engineering
from the University of Illinois at Urbana-Champaign, Champaign, IL, USA,
in 2002. He is currently a Professor with the Department of Computer
Science, University of Texas at San Antonio (UTSA), San Antonio, TX, USA.
He took a one-year faculty leave with Microsoft Research Asia, Beijing,
from 2008 to 2009. His research interests include multimedia information
retrieval and computer vision. He has authored over 260 refereed journal
and conference papers. His research projects were funded by the NSF, ARO,
DHS, SALSI, CIAS, and UTSA, and he also received faculty research awards
from Google, NEC Laboratories of America, FXPAL, Akiira Media Systems,
and HP Labs. He received the Best Paper Awards in PCM 2013, MMM
2013, and ICIMCS 2012, the Top 10% Paper Award in MMSP 2011, the
Best Student Paper in ICASSP 2006, and the Best Paper Candidate in PCM
2007. He received the 2010 ACM Service Award. He is the Guest Editor
of the IEEE TRANSACTIONS ON MULTIMEDIA, Journal of Computer Vision
and Image Understanding, Pattern Recognition Letters, EURASIP Journal on
Advances in Signal Processing, Journal of Visual Communication and Image
Representation, and is on the Editorial Board of the IEEE TRANSACTIONS ON
MULTIMEDIA, the IEEE TRANSACTIONS ON CIRCUIT AND SYSTEMS FOR
VIDEO TECHNOLOGY, Multimedia Systems Journal, Journal of Multimedia,
and Journal of Machine Visions and Applications.
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