With advances in the computer technology and the World Wide Web there has been an
explosion in the amount and complexity of multimedia data that are generated, stored,
transmitted, analyzed, and accessed. In order to extract useful information from this huge
amount of data, many content based image retrieval (CBIR) systems have been developed in the
last decade. A typical CBIR system captures image features that represent image properties
such as color, texture, or shape of objects in the query image and try to retrieve images from the
database with similar features. Retrieval efficiency and accuracy are the important issues in
designing Content Based Image Retrieval System. The Shape and Spatial features are quiet easy
and simple to derive and effective. Researchers are moving towards finding spatial features and
the scope of implementing these features in to the image retrieval framework for reducing the
semantic gap. This Survey paper focuses on the detailed review of different methods and their
evaluation techniques used in the recent works based on spatial features in CBIR systems.
Finally, several recommendations for future research directions have been suggested based on
the recent technologies.
Global Descriptor Attributes Based Content Based Image Retrieval of Query ImagesIJERA Editor
The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In this proposed system we investigate method for describing the contents of images which characterizes images by global descriptor attributes, where global features are extracted to make system more efficient by using color features which are color expectancy, color variance, skewness and texture feature correlation.
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...IJMIT JOURNAL
Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So now a days the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. With large data sets, there is possibility of high dimensionality. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging arena. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divide and conquer K Means clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed.
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
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
Faro Visual Attention For Implicit Relevance Feedback In A Content Based Imag...Kalle
In this paper we propose an implicit relevance feedback method with the aim to improve the performance of known Content Based Image Retrieval (CBIR) systems by re-ranking the retrieved images according to users’ eye gaze data. This represents a new mechanism for implicit relevance feedback, in fact usually the sources taken into account for image retrieval are based on the natural behavior of the user in his/her environment estimated by analyzing mouse and keyboard interactions. In detail, after the retrieval of the images by querying CBIRs with a keyword, our system computes the most salient regions (where users look with a greater interest) of the retrieved images by gathering data from an unobtrusive eye tracker, such as Tobii T60. According to the features, in terms of color, texture, of these relevant regions our system is able to re-rank the images, initially, retrieved by the CBIR. Performance evaluation, carried out on a set of 30 users by using Google Images and “pyramid” like keyword, shows that about the 87% of the users is more satisfied of the output images when the re-raking is applied.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphones, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem. The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic re- lationship with the query given by the user Content Based Image Retrieval Project.
http://takeoffprojects.com/content-based-image-retrieval-project
We are providing you with some of the greatest ideas for building Final Year projects with proper guidance and assistance
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...IJSRD
In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the users’ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.
Image retrieval is the major innovations in the development of images. Mining of images is used to mine latest information from
the general collection of images. CBIR is the latest method in which our target images is to be extracted on the basis of specific features of
the specified image. The image can be retrieved in fast if it is clustered in an accurate and structured manner. In this paper, we have the
combined the theories of CBIR and analysis of features of CBIR systems.
Content Based Video Retrieval Using Integrated Feature Extraction and Persona...IJERD Editor
Traditional video retrieval methods fail to meet technical challenges due to large and rapid growth of
multimedia data, demanding effective retrieval systems. In the last decade Content Based Video Retrieval
(CBVR) has become more and more popular. The amount of lecture video data on the Worldwide Web (WWW)
is growing rapidly. Therefore, a more efficient method for video retrieval in WWW or within large lecture video
archives is urgently needed. This paper presents an implementation of automated video indexing and video
search in large videodatabase. First of all, we apply automatic video segmentation and key-frame detection to
extract the frames from video. At next, we extract textual keywords by applying on video i.e. Optical Character
Recognition (OCR) technology on key-frames and Automatic Speech Recognition (ASR) on audio tracks of that
video. At next, we also extractingcolour, texture and edge detector features from different method. At last, we
integrate all the keywords and features which has extracted from above techniques for searching
purpose.Finallysearch similarity measure is applied to retrieve the best matchingcorresponding videos are
presented as output from database. Additionally we are providing Re-ranking of results as per users interest in
original result.
An adaptive threshold segmentation for detection of nuclei in cervical cells ...csandit
PAP smear test is the most efficient and easy procedure to detect any abnormality in cervical
cells. It becomes difficult for the cytologist to analyse a large set of PAP smear test images
when there is a rapid increase in the incidence of cervical cancer. On the replacement, image
analysis could swap manual interpretation. This paper proposes a method for the detection of
cervical cells in pap smear images using wavelet based thresholding. First, Wiener filter is used
for smoothing to suppress the noise and to improve the contrast of the image. Second, optimal
threshold is been obtained for segmenting the cell by various Wavelet shrinkage techniques like
VisuShrink, BayesShrink and SureShrink thresholding which segment the foreground from the
background and detect cell component like nucleus from the clustered cell images. From the
results, it is proved that the performance of the adaptive Wiener filter with combination of
SureShrink thresholding performs better in terms of threshold values and Mean Squared Error
than the other comparative methods. The succeeding research work can be carried out based on
the size of the segmented nucleus which therefore helps in differentiating abnormality among
the cells.
Global Descriptor Attributes Based Content Based Image Retrieval of Query ImagesIJERA Editor
The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In this proposed system we investigate method for describing the contents of images which characterizes images by global descriptor attributes, where global features are extracted to make system more efficient by using color features which are color expectancy, color variance, skewness and texture feature correlation.
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...IJMIT JOURNAL
Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So now a days the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. With large data sets, there is possibility of high dimensionality. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging arena. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divide and conquer K Means clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed.
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
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
Faro Visual Attention For Implicit Relevance Feedback In A Content Based Imag...Kalle
In this paper we propose an implicit relevance feedback method with the aim to improve the performance of known Content Based Image Retrieval (CBIR) systems by re-ranking the retrieved images according to users’ eye gaze data. This represents a new mechanism for implicit relevance feedback, in fact usually the sources taken into account for image retrieval are based on the natural behavior of the user in his/her environment estimated by analyzing mouse and keyboard interactions. In detail, after the retrieval of the images by querying CBIRs with a keyword, our system computes the most salient regions (where users look with a greater interest) of the retrieved images by gathering data from an unobtrusive eye tracker, such as Tobii T60. According to the features, in terms of color, texture, of these relevant regions our system is able to re-rank the images, initially, retrieved by the CBIR. Performance evaluation, carried out on a set of 30 users by using Google Images and “pyramid” like keyword, shows that about the 87% of the users is more satisfied of the output images when the re-raking is applied.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphones, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem. The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic re- lationship with the query given by the user Content Based Image Retrieval Project.
http://takeoffprojects.com/content-based-image-retrieval-project
We are providing you with some of the greatest ideas for building Final Year projects with proper guidance and assistance
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...IJSRD
In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the users’ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.
Image retrieval is the major innovations in the development of images. Mining of images is used to mine latest information from
the general collection of images. CBIR is the latest method in which our target images is to be extracted on the basis of specific features of
the specified image. The image can be retrieved in fast if it is clustered in an accurate and structured manner. In this paper, we have the
combined the theories of CBIR and analysis of features of CBIR systems.
Content Based Video Retrieval Using Integrated Feature Extraction and Persona...IJERD Editor
Traditional video retrieval methods fail to meet technical challenges due to large and rapid growth of
multimedia data, demanding effective retrieval systems. In the last decade Content Based Video Retrieval
(CBVR) has become more and more popular. The amount of lecture video data on the Worldwide Web (WWW)
is growing rapidly. Therefore, a more efficient method for video retrieval in WWW or within large lecture video
archives is urgently needed. This paper presents an implementation of automated video indexing and video
search in large videodatabase. First of all, we apply automatic video segmentation and key-frame detection to
extract the frames from video. At next, we extract textual keywords by applying on video i.e. Optical Character
Recognition (OCR) technology on key-frames and Automatic Speech Recognition (ASR) on audio tracks of that
video. At next, we also extractingcolour, texture and edge detector features from different method. At last, we
integrate all the keywords and features which has extracted from above techniques for searching
purpose.Finallysearch similarity measure is applied to retrieve the best matchingcorresponding videos are
presented as output from database. Additionally we are providing Re-ranking of results as per users interest in
original result.
An adaptive threshold segmentation for detection of nuclei in cervical cells ...csandit
PAP smear test is the most efficient and easy procedure to detect any abnormality in cervical
cells. It becomes difficult for the cytologist to analyse a large set of PAP smear test images
when there is a rapid increase in the incidence of cervical cancer. On the replacement, image
analysis could swap manual interpretation. This paper proposes a method for the detection of
cervical cells in pap smear images using wavelet based thresholding. First, Wiener filter is used
for smoothing to suppress the noise and to improve the contrast of the image. Second, optimal
threshold is been obtained for segmenting the cell by various Wavelet shrinkage techniques like
VisuShrink, BayesShrink and SureShrink thresholding which segment the foreground from the
background and detect cell component like nucleus from the clustered cell images. From the
results, it is proved that the performance of the adaptive Wiener filter with combination of
SureShrink thresholding performs better in terms of threshold values and Mean Squared Error
than the other comparative methods. The succeeding research work can be carried out based on
the size of the segmented nucleus which therefore helps in differentiating abnormality among
the cells.
Node failure time analysis for maximum stability vs minimum distance spanning...csandit
A mobile sensor network is a wireless network of sensor nodes that move arbitrarily. In this
paper, we explore the use of a maximum stability spanning tree-based data gathering
(Max.Stability-DG) algorithm and a minimum-distance spanning tree-based data gathering
(MST-DG) algorithm for mobile sensor networks. We analyze the impact of these two
algorithms on the node failure times, specifically with respect to the node lifetime (the time of
first node failure) and network lifetime (the time of disconnection of the network of live sensor
nodes due to one or more node failures). Both the Max.Stability-DG and MST-DG algorithms
are based on a greedy strategy of determining a data gathering tree when one is needed and
using that tree as long as it exists. The Max.Stability-DG algorithm assumes the availability of
the complete knowledge of future topology changes and determines a data gathering tree whose
corresponding spanning tree would exist for the longest time since the current time instant;
whereas, the MST-DG algorithm determines a data gathering tree whose corresponding
spanning tree is the minimum distance tree at the current time instant. We observe a node
lifetime – network lifetime tradeoff: the Max.Stability-DG trees incur a lower node lifetime due
to repeated use of a data gathering tree for a longer time; on the other hand, the Max.Stability-
DG trees incur a longer network lifetime
Early screening of Diabetes Mellitus can prevent the unwanted complications of Diabetes Mellitus like diabetic nephropathy, diabetic retinopathy, diabetic foot, etc.
Er. Uttam Raj Timilsina(MSc.Engineering,IIT Roorkee)
Professor of Agricultural Engineering,Agriculture and Forestry University (AFU), Rampur, Chitwan, Nepal
uttamrajtimilsina@gmail.com
*All Right Reserved**
Uploaded and Shared by AgriYouthNepal
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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
Content Based Image Retrieval (CBIR) aims at retrieving the images from the database based on the user query which is visual form rather than the traditional text form. The applications of CBIR extend from surveillance to remote sensing, medical imaging to weather forecasting, and security systems to historical research and so on. Though extensive research is made on content based image retrieval in the spatial domain, we have most images in the internet which is JPEG compressed which pushes the need for image retrieval in the compressed domain itself rather than decoding it to raw format before comparison and retrieval. This research addresses the need to retrieve the images from the database based on the features extracted from the compressed domain along with the application of genetic algorithm in improving the retrieval results. The research focuses on various features and their levels of impact on improving the precision and recall parameters of the CBIR system. Our experimentation results also indicate that the CBIR features in compressed domain along with the genetic algorithm usage improves the results considerably when compared with the literature techniques.
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
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.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
Similar to Applications of spatial features in cbir a survey (16)
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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both traditional databases and text based image understanding systems. The challenge in CBIR is
to develop the methods that will increase the retrieval accuracy and reduce the retrieval time.
Content based image retrieval system consists of following modules:
1. Feature Extraction: In this module the features of interest are calculated for image
database. This step is done in offline manner.
2. Feature extraction of query image: This module calculates the feature of the query
image. Query image can be a part of image database or it may not be a part of image
database.
3. Similarity measure: This module compares the feature database of the existing images
with the query image on basis of the similarity measure of the interest.
4. Retrieval and Result: This module will display the matching images to the user.
In order to improve the effectiveness of CBIR systems we need to discover semantically
meaningful patterns. Semantic concept detection is a research topic of great interest as it provides
semantic filters to help analysis and search of multimedia data. It is essentially a classification
task that determines whether an image or a video shot is relevant to a given semantic concept.
The semantic concepts cover a wide range of topics such as those related to objects,
indoor/outdoor scenes, events, etc. Automatically detecting these concepts is challenging
especially in the presence of within-class variation, occlusion, background clutter, pose and
lighting changes in images and video shots. Global features are known to be limited in face of
these difficulties, which stimulated the development of local invariant features (key points) in
recent years. Key points are salient patches that contain rich local information about an image.
The most popular key point based representation is bag-of-visual-words (BoW). In BoW, a visual
vocabulary is generated through grouping similar key points into a large number of clusters and
treating each cluster as a visual word. By mapping the key points of an image back into visual
words of the vocabulary, we can represent the image as a histogram of visual words and use it as
the feature for classification.
1.1 Feature Extraction
Mapping the image pixels into the feature space is known as feature extraction. Extracted features
are used to represent images for searching, indexing and browsing images in an image database.
Feature extraction is a means of extracting compact but semantically valuable information from
images. This information is used as a signature for the image. Similar images should have similar
signatures. Feature extraction of the image in the database is typically conducted off-line so
computation complexity is not a significant issue. This section introduces spatial features: texture,
shape, and color, size and Edge density which are used most often to extract the features of an
image. We have surveyed some already existing systems to understand the various feature
extraction techniques.
In boundary detection method, the binary image is scanned until a boundary is found. A color
averaging technique is used for feature extraction in the spatial domain to reduce the size of the
feature vector. In Gradient and 12 Directional Feature Extraction methods first they compute the
gradient of the handwritten character by applying Sobel’s Mask. After computing the gradient of
each pixel of the character, they map these gradient values onto 12 direction values with angle
span of 30 degree between any two adjacent direction values .Once they get the direction
features, they feed them into a neural network system. Principal Component Analysis method for
feature extraction is used for performing dimensionality reduction. Slope Magnitude technique is
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used for Extraction of Shape Features. Fast Fourier Transform, Walsh Transform, DCT and DST
transform is also used for feature Extraction to design the sectors required for the search and
retrieval of images from a database.
1.2 Feature Selection
It is generally believed that a better image recognition effect can be achieved with more features
descriptors used, but this is not absolutely true. Not all features are helpful for image recognition.
Irrelevant features are actually interfering signals and cause drops in the recognition rate,
especially if the effect of the irrelevant features exceeds that of the effective ones. Therefore,
these features must be dropped. The goal of feature selection technique is to select the best
features from the original ones. It can not only achieve maximum recognition rate but can also
simplify the calculation of the image retrieval process. Working with extremely high dimensional
data, as given in case of a spectral data set, may result in high computational costs. Furthermore
we have to face the so-called “curse of dimensionality”, as the number of samples in relation to
the number of features is very low. Therefore, researchers perform dimensionality reduction or
feature selection where either a subset or a transform of the original features is chosen for the
clustering process.
1.3 Similarity Measurements
The CBIR process is used for general applications like recognizing patterns and matching
fingerprints or other biometric data. Basically we need to compare two images and check whether
they are similar or they match or not. To compute this, it is required to have certain techniques by
which one can statistically evaluate if two Images are similar or not. Once we extract a good set
of features, we compare the extracted feature for similarity. It is believed that if good sets of
features are extracted, the similarity between 2 images is given by how close the extracted
features are of the two images. There are various kinds of similarity measurement techniques: the
Euclidean Distance, Mean Square Error and Sum of Absolute Differences are common for CBIR
applications.
2. RELATED WORKS
Semantic concept detection aims to annotate images or video shots with respect to a semantic
concept. In existing Works, this task is often conducted in a diverse setting where the emphasis
usually includes feature selection, multimodality fusion, and machine learning on huge
multimedia data sets. Here we focus our review on feature-level analysis which is related to our
latter experimental comparison. In [1] Edge Histogram Descriptor (EHD) captures the spatial
distribution of edges like the color layout descriptor does by capturing colors. It is useful in image
matching when the texture is not homogeneous. A novel image feature representation method,
namely color difference histograms (CDH) has been proposed in [2] for image retrieval. This
method is entirely different from the existing histograms; most of the existing histogram
techniques merely count the number of frequency of pixels.
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Fig.2. An example of image retrieval using the CDH algorithm on the Corel 10Kdataset.Thequery is an
image of fruit, and all the returned images are correctly retrieved and ranked within the top12images.(The
top left image is the query image, and the similar images returned include the query image itself) as in [2].
In [3] three approaches to utilizing object-level spatial contextual information for semantic image
analysis are presented and comparatively evaluated. Contextual information is in the form of
fuzzy directional relations between image regions. All techniques, namely a Genetic Algorithm
(GA), a Binary Integer Programming (BIP) and an Energy-Based Model (EBM), are applied in
order to estimate an optimal semantic image interpretation, after an initial set of region
classification results is computed using solely visual features.
Fig.3. Developed Evaluation Framework as in [3]
In [4] an effective descriptor based on Angular Radial Transform (ART) and Polar Hough
Transform (PHT), which is capable of fulfilling the above requirements. ART is used as a region
based shape descriptor, which represents the global aspects of an image is proposed. PHT is used
as a local shape descriptor for detecting linear edges in an edge image. The detected linear edges
represent the association among adjacent edge points. The perpendicular distance of each linear
edge to the centroid of the edge image is computed to build histograms, which exhibit rotation,
scale and translation invariant properties.
A new image indexing and retrieval system for CBIR is proposed in [5]. The characteristics of
image are computed using color histogram and spatial orientation tree (SOT). The SOT defines
the spatial parent–child relationship among wavelet coefficients in multi-resolution wavelet sub-
5. Computer Science & Information Technology (CS & IT) 307
bands. First the image is divided into sub-blocks and then constructed the SOT for each low pass
wavelet coefficient is considered as a vector point of that particular image. Similarly the color
histogram features are collected from the each sub-block. The vector points of each image are
indexed using vocabulary tree. Vector points of each image are indexed using vocabulary tree. A
specific CBIR system for hyper spectral images exploiting its rich spectral information is
proposed on [7]. The CBIR image features are the end member signatures obtained from the
image data by end member induction algorithms (EIAs). End members correspond to the
elementary materials in the scene, so that the pixel spectra can be decomposed into a linear
combination of end member signatures.
Fig.4.Precision and recall results for the10-Edataset using the EIHA end member induction algorithm for
the Granadistance and the Hausdorff distances in [6]
3. OVERVIEW OF PROPOSED METHOD
The spatial locations are the key points in an image carry important information for classifying
the image. For example, an image showing a beach scene typically consists of sky-like key points
on the top and sands-like key points at the bottom. To integrate the spatial information, we follow
to partition an image into equalized rectangular regions, compute the visual-word feature from
each region, and concatenate the features of these regions into a single feature vector. There can
be many ways of partitioning, e.g., 3x3 means cutting an image into 9 regions. This region-based
representation has its downside in terms of cost and generalizability. First, if we divide each
image into M x N regions, and compute a K-dimensional feature on each region, the concatenated
feature vector is of K x M x N dimension, which can be prohibitively expensive to deal with.
Besides, encoding spatial information can make the representation less generalizable. Suppose an
image class is defined by the presence of a certain object, say, airplane, which may appear
anywhere in an image. Using region-based representation can cause a feature mismatch if the
objects in the training images are in different regions from those in the testing images. Many
objects may cross region boundaries. These considerations prefer relatively coarse partitions of
image regions to fine-grained partitions.
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4. EXPERIMENTAL RESULTS AND DISCUSSION
Combining the important sub features of color, texture and shape improves the efficiency of
image retrieval .PCA is used for feature selection which results in reduced set of features which is
the set of selected features. These reduced features can be used for image retrieval and give the
better performance[1].Experimental results demonstrate that CDH is much more efficient than the
existing image feature descriptors that were originally developed for content-based image
retrieval, such as MPEG-7 edge histogram descriptors, color auto correlograms and multi-texton
histograms. It has a strong discriminative power using the color, texture and shape features while
accounting for spatial layout [2]. Spatial context is efficient in improving the initial region-
concept association results exhibiting an overall increase of up to 9.25% in the current evaluation
framework by exploiting spatial context in semantic image analysis. The highest on average
performance is achieved when complex partial constraints are acquired and their weight against
the visual and co-occurrence information is efficiently adjusted [3].The combination of both
global (ART based) and local (PHT based) features yields improved retrieval accuracy, which is
analyzed over various sorts of image databases. An extensive set of experiments witness the
superiority of the proposed hybrid system over other major contour based, region based and
hybrid approaches in [4].The importance of spatial information can be seen by comparing the
classification performance between the plain visual-word features (BoW) and the region-based
ones. four ways of partitioning images, including 1x1(whole image), 2x2 (4 regions), 3x3 (9
regions), and 4x4 (16 regions) is used. Figure 6 shows the performance using different spatial
partitions on various key point representation techniques such as vocabulary sizes, and weighting
schemes. We see that the 2x2 partition substantially improves the classification performance. As
the partition changes from 2x2 to 4 x 4, the MinfAP drops for most of the vocabulary sizes. When
investigating the per-concept performance, we find that spatial information is more useful for
classifying scenes than for classifying objects, since the former usually occupy a whole key
frame, while the latter can appear anywhere in a key frame. For large scale semantic detection in
diversified data set, using 2x2 partitions might be enough. The spatial information always helps
as we have investigated various representation choices in BoW feature for semantic concept
detection.
Fig.5.Concept detection performance on TRECVID 2006 using region-based features computed from
different spatial partitions. Due to the feature mismatch problem caused by spatial partition, relatively
coarse region partition such as 2x2 is preferred.
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5. APPLICATIONS
The CBIR technology has been used in several applications such as fingerprint identification,
biodiversity information systems, digital libraries, crime prevention, medicine, historical research.
Various applications of CBIR and the approaches used are shown in the Table 1. The selection of
the CBIR process depends on many constraints and has to be chosen to best suit the final goal.
Constant efforts are being made to reach a 100% accuracy rate.
Table 1.Applications of CBIR
6. INFERENCES MADE
• EDH technique can be extended to spectral and frequency domain to extract the features.
• In addition to spatial context exploitation, additional contextual information sources and
their combination with spatial context can be explored.
• Due to data variations due to the image capture condition study of appropriate image
normalization procedures
• Developing relevance feedback CBIR approaches for hyper spectral images.
• By mining semantically meaningful patterns we can improve the image search and
retrieval process.
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7. CONCLUSION AND FUTURE WORK
In this paper we reviewed about various feature extraction methods, It has been found that
variation in feature extraction methodologies can ensure the better and more accurate retrieval of
relevant images from the large database. We have also explored the feature set in spatial domain
of CBIR system. In every kind of application we strive to achieve a 100% accurate retrieval rate.
Lots of research is being currently done for the improvement in the feature extraction methods. In
future the features can be extracted using spectral and frequency domain and we can also extract
the image by combining all the domains. Investigation of additional contextual information
sources, like scene type related information, and their combination with spatial context for
achieving will also further improve the performance of Image Retrieval Systems. In order to
improve the effectiveness of CBIR systems we need to discover semantically meaningful
patterns. The spatial locations are the key points in an image carry important information for
classifying the image. By integrating the spatial information with key point selection technique
we can detect semantic concept from which semantically meaningful patterns can be discovered.
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