The document discusses two approaches to automatically annotating images with semantic keywords: supervised one-vs-all (OVA) labeling and unsupervised labeling. Supervised OVA trains independent binary classifiers for each keyword, while unsupervised labeling treats annotation as estimating a joint probability model of image features and keywords from training data. The proposed approach combines advantages of both by formulating annotation as a multi-class classification problem, retaining optimality of supervised OVA but with weaker labeling requirements and lower complexity like unsupervised methods.
This document describes a new deep learning model called Convolutional eXtreme Gradient Boosting (ConvXGB) that combines a Convolutional Neural Network (CNN) and XGBoost for classification problems. ConvXGB consists of stacked convolutional layers for feature learning, followed by XGBoost in the last layer for class prediction. Experiments on image and general datasets showed ConvXGB achieved slightly better accuracy than CNN and XGBoost alone, and was sometimes significantly better.
Performance Evaluation of Object Tracking Technique Based on Position VectorsCSCJournals
This document presents a novel algorithm for object tracking in video frames based on position vectors. The algorithm first extracts position vectors for the object in the first frame. It then tracks the object across subsequent frames by cropping each new frame into blocks based on shifted position vectors and performing block matching between frames using feature vectors extracted by discrete wavelet transform (DWT) or dual tree complex wavelet transform (DTCWT). Experimental results on video sequences show the algorithm using DTCWT achieves higher tracking precision (95%) compared to DWT (92%). The algorithm is computationally efficient and can track multiple moving and still objects.
FUZZY SET THEORETIC APPROACH TO IMAGE THRESHOLDINGIJCSEA Journal
Thresholding is a fast, popular and computationally inexpensive segmentation technique that is always critical and decisive in some image processing applications. The result of image thresholding is not always satisfactory because of the presence of noise and vagueness and ambiguity among the classes. Since the theory of fuzzy sets is a generalization of the classical set theory, it has greater flexibility to capture faithfully the various aspects of incompleteness or imperfectness in information of situation. To overcome this problem, in this paper we proposed a two-stage fuzzy set theoretic approach to image thresholding utilizing the measure of fuzziness to evaluate the fuzziness of an image and to determine an adequate threshold value. At first, images are preprocessed to reduce noise without any loss of image details by fuzzy rule-based filtering and then in the final stage a suitable threshold is determined with the help of a fuzziness measure as a criterion function. Experimental results on test images have demonstrated the effectiveness of this method.
An Information Maximization approach of ICA for Gender ClassificationIDES Editor
In this paper, a novel and successful method for
gender classification from human faces using dimensionality
reduction technique is proposed. Independent Component
Analysis (ICA) is one of such techniques. In the current
scheme, a thrust is given on the different algorithms and
architectures of ICA. An information maximization ICA is
discussed with its two architecture and compared with the two
architectures of fast ICA. Support Vector Machine (SVM) is
used as a classifier for the separation of male and female
classes. All experiments are done on FERET database. Results
are obtained for the different combinations of train and test
database sizes. For larger
training set SVM is performing with an accuracy of 98%. The
accuracy values are varied for change in size of testing set and
the proposed system performs with an average accuracy of
96%. An improvement in performance is achieved using class
discriminability which performs with 100% accuracy.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
The document describes an algorithm called X-TREPAN that extracts decision trees from trained neural networks. X-TREPAN is an enhancement of the TREPAN algorithm that allows it to handle both multi-class classification and multi-class regression problems. It can also analyze generalized feed forward networks. The algorithm was tested on several real-world datasets and was found to generate decision trees with good classification accuracy while also maintaining comprehensibility.
The document discusses an algorithm called Adaptive Multichannel Component Analysis (AMMCA) for separating image sources from mixtures using adaptively learned dictionaries. It begins by reviewing image denoising using learned dictionaries, then extends this to image separation from single mixtures. The key contribution is applying this approach to separating sources from multichannel mixtures by learning local dictionaries for each source during the separation process. The algorithm is described and simulated results are shown separating two images from a noisy mixture using the learned dictionaries. In conclusion, AMMCA is able to separate sources without prior knowledge of their sparsity domains by fusing dictionary learning into the separation process.
The document provides information about cluster analysis and hierarchical agglomerative clustering. It discusses how hierarchical agglomerative clustering works by successively merging the most similar clusters together based on a distance measure between clusters. It begins with each object as its own cluster and merges them into larger clusters, forming a dendrogram, until all objects are in a single cluster. Different linkage criteria like single, complete, and average linkage can be used to define the distance between clusters.
PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL ...ijaia
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
This document describes a new deep learning model called Convolutional eXtreme Gradient Boosting (ConvXGB) that combines a Convolutional Neural Network (CNN) and XGBoost for classification problems. ConvXGB consists of stacked convolutional layers for feature learning, followed by XGBoost in the last layer for class prediction. Experiments on image and general datasets showed ConvXGB achieved slightly better accuracy than CNN and XGBoost alone, and was sometimes significantly better.
Performance Evaluation of Object Tracking Technique Based on Position VectorsCSCJournals
This document presents a novel algorithm for object tracking in video frames based on position vectors. The algorithm first extracts position vectors for the object in the first frame. It then tracks the object across subsequent frames by cropping each new frame into blocks based on shifted position vectors and performing block matching between frames using feature vectors extracted by discrete wavelet transform (DWT) or dual tree complex wavelet transform (DTCWT). Experimental results on video sequences show the algorithm using DTCWT achieves higher tracking precision (95%) compared to DWT (92%). The algorithm is computationally efficient and can track multiple moving and still objects.
FUZZY SET THEORETIC APPROACH TO IMAGE THRESHOLDINGIJCSEA Journal
Thresholding is a fast, popular and computationally inexpensive segmentation technique that is always critical and decisive in some image processing applications. The result of image thresholding is not always satisfactory because of the presence of noise and vagueness and ambiguity among the classes. Since the theory of fuzzy sets is a generalization of the classical set theory, it has greater flexibility to capture faithfully the various aspects of incompleteness or imperfectness in information of situation. To overcome this problem, in this paper we proposed a two-stage fuzzy set theoretic approach to image thresholding utilizing the measure of fuzziness to evaluate the fuzziness of an image and to determine an adequate threshold value. At first, images are preprocessed to reduce noise without any loss of image details by fuzzy rule-based filtering and then in the final stage a suitable threshold is determined with the help of a fuzziness measure as a criterion function. Experimental results on test images have demonstrated the effectiveness of this method.
An Information Maximization approach of ICA for Gender ClassificationIDES Editor
In this paper, a novel and successful method for
gender classification from human faces using dimensionality
reduction technique is proposed. Independent Component
Analysis (ICA) is one of such techniques. In the current
scheme, a thrust is given on the different algorithms and
architectures of ICA. An information maximization ICA is
discussed with its two architecture and compared with the two
architectures of fast ICA. Support Vector Machine (SVM) is
used as a classifier for the separation of male and female
classes. All experiments are done on FERET database. Results
are obtained for the different combinations of train and test
database sizes. For larger
training set SVM is performing with an accuracy of 98%. The
accuracy values are varied for change in size of testing set and
the proposed system performs with an average accuracy of
96%. An improvement in performance is achieved using class
discriminability which performs with 100% accuracy.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
The document describes an algorithm called X-TREPAN that extracts decision trees from trained neural networks. X-TREPAN is an enhancement of the TREPAN algorithm that allows it to handle both multi-class classification and multi-class regression problems. It can also analyze generalized feed forward networks. The algorithm was tested on several real-world datasets and was found to generate decision trees with good classification accuracy while also maintaining comprehensibility.
The document discusses an algorithm called Adaptive Multichannel Component Analysis (AMMCA) for separating image sources from mixtures using adaptively learned dictionaries. It begins by reviewing image denoising using learned dictionaries, then extends this to image separation from single mixtures. The key contribution is applying this approach to separating sources from multichannel mixtures by learning local dictionaries for each source during the separation process. The algorithm is described and simulated results are shown separating two images from a noisy mixture using the learned dictionaries. In conclusion, AMMCA is able to separate sources without prior knowledge of their sparsity domains by fusing dictionary learning into the separation process.
The document provides information about cluster analysis and hierarchical agglomerative clustering. It discusses how hierarchical agglomerative clustering works by successively merging the most similar clusters together based on a distance measure between clusters. It begins with each object as its own cluster and merges them into larger clusters, forming a dendrogram, until all objects are in a single cluster. Different linkage criteria like single, complete, and average linkage can be used to define the distance between clusters.
PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL ...ijaia
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
The document compares image classification in ENVI and eCognition software. It details the image classification process for each: in ENVI, images are corrected, filtered, and then classified as supervised or unsupervised; in eCognition, images undergo multiresolution segmentation into objects before creating classes and performing nearest neighbor classification. The conclusion states that ENVI classification is more standardized and supervised, while eCognition uses an object-oriented, hierarchical, trial-and-error approach to segmentation and classification.
Abstract: Object Classification is an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined categories. Classification includes image sensors, image pre-processing, object detection, object segmentation, feature extraction and object classification. Many classification techniques have been developed for image classification. In this survey various classification techniques are considered; Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM) and Fuzzy Classification.Keywords: Image Classification, Artificial Neural Network, Decision Tree, Support Vector Machine, Fuzzy Classifier.
Title: Analysis of Classification Approaches
Author: Robin Kumar
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
This document contains a summary of an advanced image classification workshop presentation. It discusses pixel-based and object-based image classification techniques. Pixel-based classification involves classifying pixels based on their spectral values using supervised or unsupervised classification methods. Supervised classification uses training data to develop algorithms to classify pixels, while unsupervised classification automatically groups pixels into clusters. Object-based classification considers both spectral and spatial characteristics of grouped pixels.
Computer Vision: Visual Extent of an ObjectIOSR Journals
The document discusses analyzing the visual extent of objects in images using computer vision techniques. It performs two analyses: 1) Without knowing object locations, it determines which image parts contribute most to object classification. 2) Assuming known object locations, it evaluates the potential of object vs. surround and object interior vs. border. The analyses find that object classification performance improves significantly when the object location is known. Descriptors from the object interior contribute more than those from the border. Knowing object locations allows separating relevant from irrelevant image regions.
This document presents a survey of contemporary research on image segmentation through clustering techniques. It discusses various clustering approaches including exclusive clustering (e.g. k-means), overlapping clustering (e.g. fuzzy c-means), hierarchical clustering, and probabilistic D-clustering. It provides details on the algorithms and steps involved in each technique. The paper analyzes different clustering methods for image segmentation and concludes that fuzzy c-means is superior but has high computational costs, while probabilistic D-clustering can avoid this issue.
Kandemir Inferring Object Relevance From Gaze In Dynamic ScenesKalle
As prototypes of data glasses having both data augmentation and gaze tracking capabilities are becoming available, it is now possible to develop proactive gaze-controlled user interfaces to display information about objects, people, and other entities in real-world setups. In order to decide which objects the augmented information should be about, and how saliently to augment, the system needs an estimate of the importance or relevance of the objects of the scene for the user at a given time. The estimates will be used to minimize distraction of the user, and for providing efficient spatial management of the augmented items. This work is a feasibility study on inferring the relevance of objects in dynamic scenes from gaze. We collected gaze data from subjects watching a video for a pre-defined task. The results show that a simple ordinal logistic regression model gives relevance rankings of scene objects with a promising accuracy.
A Review of Image Classification TechniquesIRJET Journal
This document provides a review of various image classification techniques. It begins by defining image classification as the process of assigning pixels to finite classes based on their data values. The techniques can be categorized as supervised or unsupervised. Supervised techniques use training data to define decision boundaries, while unsupervised techniques automatically partition data without labels. Common supervised techniques discussed include parallelpiped, minimum distance, and maximum likelihood classification. Unsupervised techniques include hierarchical and partitioning clustering. The document also explores hard and soft classifiers, and how combinations of techniques can improve accuracy over single methods.
Islamic University Pattern Recognition & Neural Network 2019 Rakibul Hasan Pranto
The document discusses various topics related to pattern recognition including:
1. Pattern recognition is the automated recognition of patterns and regularities in data through techniques like machine learning. It has applications in areas like optical character recognition, diagnosis systems, and security.
2. There are two main approaches to pattern recognition - sub-symbolic and symbolic. Sub-symbolic uses connectionist models like neural networks while symbolic uses formal structures like strings and automata to represent patterns.
3. A pattern recognition system consists of steps like data acquisition, pre-processing, feature extraction, model learning, classification, and post-processing to classify patterns. Bayesian decision making and Bayes' theorem are statistical techniques used in classification.
A Fuzzy Set Approach for Edge DetectionCSCJournals
Image segmentation is one of the most studied problems in image analysis, computer vision, pattern recognition etc. Edge detection is a discontinuity based approach used for image segmentation. In this paper, an edge detection using fuzzy set is proposed, where an image is considered as a fuzzy set and pixels are taken as elements of fuzzy set. The fuzzy approach converts the color image to a partially segmented image; finally an edge detector is convolved over the partially segmented image to obtain an edged image. The approach is implemented using MATLAB 7.11. (R2010b). For qualitative and quantitative comparison, BSD (Berkeley Segmentation Database) images are used for experimentation. Performance parameters used are PSNR (dB) and Performance ratio (PR) of true to false edges. It has been shown that the proposed approach performs better than Canny’s edge detection algorithm under almost all scenarios. The proposed approach reduces false edge detection and double edges.
This document provides a 3-paragraph summary of support vector machines (SVMs). It begins with a brief history of SVMs from the 1990s work of Russian mathematicians Vapnik and Chervonenkis. It then explains how SVMs find the optimal separating hyperplane for classification problems, including methods for non-linear and non-separable data using kernel functions and cost functions. It concludes by noting applications of SVMs include text recognition, face detection, gene expression analysis, and music information retrieval tasks.
This document presents a novel method for recognizing two-dimensional QR barcodes using texture feature analysis and neural networks. It first extracts texture features like mean, standard deviation, smoothness, skewness and entropy from divided blocks of barcode images. These features are then used to train a neural network to classify blocks as containing a barcode or not. The trained neural network can then be used to locate barcodes in unknown images by classifying each block. The method is implemented and evaluated using MATLAB on a database of QR code images, showing satisfactory recognition results.
https://imatge.upc.edu/web/publications/efficient-exploration-region-hierarchies-semantic-segmentation
The motivation of this work is the efficient exploration of hierarchical partitions for semantic segmentation as a method for locating objects in images. While many efforts have been focused on efficient image search in large-scale databases, few works have addressed the problem of locating and recognizing objects efficiently within a given image. My work considers as an input a hierarchical partition of an image that defines a set of regions as candidate locations to contain an object. This approach will be compared to other state of the art algorithms that extract object candidates for an image. The final goal of this work is to semantically segment images efficiently by exploiting the multiscale information provided by a hierarchical partition, maximizing the accuracy of the segmentation when only a very few regions of the partition are analysed.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
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
INTRA BLOCK AND INTER BLOCK NEIGHBORING JOINT DENSITY BASED APPROACH FOR JPEG...ijsc
Steganalysis is the method used to detect the presence of any hidden message in a cover medium. A novel
approach based on feature mining on the discrete cosine transform (DCT) domain based approach,
machine learning for steganalysis of JPEG images is proposed. The neighboring joint density on both
intra-block and inter-block are extracted from the DCT coefficient array. After the feature space has been
constructed, it uses SVM like binary classifier for training and classification. The performance of the
proposed method on different Steganographic systems named F5, Pixel Value Differencing, Model Based
Steganography with and without deblocking, JPHS, Steghide etc are analyzed. Individually each feature
and combined features classification accuracy is checked and concludes which provides better
classification.
Shallow vs. Deep Image Representations: A Comparative Study with Enhancements...CSCJournals
This document compares shallow and deep image representations for object recognition. It discusses the traditional pipeline approach using handcrafted features extracted via local feature detectors and descriptors, then encoded and pooled. It proposes enhancements to this pipeline by augmenting features. It also discusses end-to-end deep learning models that learn representations directly from images in multiple layers without prior domain knowledge. The purpose is to compare shallow and deep representations, and improve results by combining deep models in an ensemble.
Improved Performance of Unsupervised Method by Renovated K-MeansIJASCSE
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed K-Means algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.
The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, the existing descriptors are either too computationally expensive to achieve real-time performance, or not sufficiently distinctive to identify correct matches from a large database with various transformations. In this paper, we propose a highly efficient and distinctive binary descriptor, called local difference binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pair wise grid cells within the patch. A multiple-gridding strategy and a salient bit-selection method are applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that compared to the existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieving a greater accuracy and faster speed for mobile object recognition and tracking tasks.
The Longest Day Event 2015 - Alzheimer’s AssociationScott Robarge
Scott Robarge founded a recruiting firm in 2010 called Another8 that finds talent for startups. He also contributes to the Alzheimer's Association, which will hold its annual The Longest Day event on June 21st to raise money for Alzheimer's research. Participating teams will do fundraising activities from sunrise to sunset and are asked to raise a minimum of $1,600; past activities have included cycling, bowling, and art shows tailored to interests of those with Alzheimer's.
Ensayo de derecho agrario 2da evaluaciónjm11540042
Este documento resume los conceptos clave del derecho agrario según varios autores y describe los principios constitucionales y legales que lo sustentan. Explica que el derecho agrario regula todo lo relacionado con la producción agrícola, los agricultores, la explotación de la tierra y la protección de los recursos naturales. También identifica a los sujetos involucrados en las relaciones jurídicas agrarias, como productores, no productores, sujetos positivos y negativos.
El documento describe un evento organizado por la Academia de Educación Pure Love en Madrid, España. El evento incluirá presentaciones sobre el fundador del Movimiento de Unificación para la Paz Mundial y su autobiografía, exposiciones de 5 organizaciones y personalidades premiadas por su contribución a la sociedad, una oración y brindis por la paz mundial, y la entrega de premios. El objetivo del evento es apoyar la visión de la Dra. Hak Ja Han Moon de contribuir a la paz mundial en América Latina y España.
The document compares image classification in ENVI and eCognition software. It details the image classification process for each: in ENVI, images are corrected, filtered, and then classified as supervised or unsupervised; in eCognition, images undergo multiresolution segmentation into objects before creating classes and performing nearest neighbor classification. The conclusion states that ENVI classification is more standardized and supervised, while eCognition uses an object-oriented, hierarchical, trial-and-error approach to segmentation and classification.
Abstract: Object Classification is an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined categories. Classification includes image sensors, image pre-processing, object detection, object segmentation, feature extraction and object classification. Many classification techniques have been developed for image classification. In this survey various classification techniques are considered; Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM) and Fuzzy Classification.Keywords: Image Classification, Artificial Neural Network, Decision Tree, Support Vector Machine, Fuzzy Classifier.
Title: Analysis of Classification Approaches
Author: Robin Kumar
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
This document contains a summary of an advanced image classification workshop presentation. It discusses pixel-based and object-based image classification techniques. Pixel-based classification involves classifying pixels based on their spectral values using supervised or unsupervised classification methods. Supervised classification uses training data to develop algorithms to classify pixels, while unsupervised classification automatically groups pixels into clusters. Object-based classification considers both spectral and spatial characteristics of grouped pixels.
Computer Vision: Visual Extent of an ObjectIOSR Journals
The document discusses analyzing the visual extent of objects in images using computer vision techniques. It performs two analyses: 1) Without knowing object locations, it determines which image parts contribute most to object classification. 2) Assuming known object locations, it evaluates the potential of object vs. surround and object interior vs. border. The analyses find that object classification performance improves significantly when the object location is known. Descriptors from the object interior contribute more than those from the border. Knowing object locations allows separating relevant from irrelevant image regions.
This document presents a survey of contemporary research on image segmentation through clustering techniques. It discusses various clustering approaches including exclusive clustering (e.g. k-means), overlapping clustering (e.g. fuzzy c-means), hierarchical clustering, and probabilistic D-clustering. It provides details on the algorithms and steps involved in each technique. The paper analyzes different clustering methods for image segmentation and concludes that fuzzy c-means is superior but has high computational costs, while probabilistic D-clustering can avoid this issue.
Kandemir Inferring Object Relevance From Gaze In Dynamic ScenesKalle
As prototypes of data glasses having both data augmentation and gaze tracking capabilities are becoming available, it is now possible to develop proactive gaze-controlled user interfaces to display information about objects, people, and other entities in real-world setups. In order to decide which objects the augmented information should be about, and how saliently to augment, the system needs an estimate of the importance or relevance of the objects of the scene for the user at a given time. The estimates will be used to minimize distraction of the user, and for providing efficient spatial management of the augmented items. This work is a feasibility study on inferring the relevance of objects in dynamic scenes from gaze. We collected gaze data from subjects watching a video for a pre-defined task. The results show that a simple ordinal logistic regression model gives relevance rankings of scene objects with a promising accuracy.
A Review of Image Classification TechniquesIRJET Journal
This document provides a review of various image classification techniques. It begins by defining image classification as the process of assigning pixels to finite classes based on their data values. The techniques can be categorized as supervised or unsupervised. Supervised techniques use training data to define decision boundaries, while unsupervised techniques automatically partition data without labels. Common supervised techniques discussed include parallelpiped, minimum distance, and maximum likelihood classification. Unsupervised techniques include hierarchical and partitioning clustering. The document also explores hard and soft classifiers, and how combinations of techniques can improve accuracy over single methods.
Islamic University Pattern Recognition & Neural Network 2019 Rakibul Hasan Pranto
The document discusses various topics related to pattern recognition including:
1. Pattern recognition is the automated recognition of patterns and regularities in data through techniques like machine learning. It has applications in areas like optical character recognition, diagnosis systems, and security.
2. There are two main approaches to pattern recognition - sub-symbolic and symbolic. Sub-symbolic uses connectionist models like neural networks while symbolic uses formal structures like strings and automata to represent patterns.
3. A pattern recognition system consists of steps like data acquisition, pre-processing, feature extraction, model learning, classification, and post-processing to classify patterns. Bayesian decision making and Bayes' theorem are statistical techniques used in classification.
A Fuzzy Set Approach for Edge DetectionCSCJournals
Image segmentation is one of the most studied problems in image analysis, computer vision, pattern recognition etc. Edge detection is a discontinuity based approach used for image segmentation. In this paper, an edge detection using fuzzy set is proposed, where an image is considered as a fuzzy set and pixels are taken as elements of fuzzy set. The fuzzy approach converts the color image to a partially segmented image; finally an edge detector is convolved over the partially segmented image to obtain an edged image. The approach is implemented using MATLAB 7.11. (R2010b). For qualitative and quantitative comparison, BSD (Berkeley Segmentation Database) images are used for experimentation. Performance parameters used are PSNR (dB) and Performance ratio (PR) of true to false edges. It has been shown that the proposed approach performs better than Canny’s edge detection algorithm under almost all scenarios. The proposed approach reduces false edge detection and double edges.
This document provides a 3-paragraph summary of support vector machines (SVMs). It begins with a brief history of SVMs from the 1990s work of Russian mathematicians Vapnik and Chervonenkis. It then explains how SVMs find the optimal separating hyperplane for classification problems, including methods for non-linear and non-separable data using kernel functions and cost functions. It concludes by noting applications of SVMs include text recognition, face detection, gene expression analysis, and music information retrieval tasks.
This document presents a novel method for recognizing two-dimensional QR barcodes using texture feature analysis and neural networks. It first extracts texture features like mean, standard deviation, smoothness, skewness and entropy from divided blocks of barcode images. These features are then used to train a neural network to classify blocks as containing a barcode or not. The trained neural network can then be used to locate barcodes in unknown images by classifying each block. The method is implemented and evaluated using MATLAB on a database of QR code images, showing satisfactory recognition results.
https://imatge.upc.edu/web/publications/efficient-exploration-region-hierarchies-semantic-segmentation
The motivation of this work is the efficient exploration of hierarchical partitions for semantic segmentation as a method for locating objects in images. While many efforts have been focused on efficient image search in large-scale databases, few works have addressed the problem of locating and recognizing objects efficiently within a given image. My work considers as an input a hierarchical partition of an image that defines a set of regions as candidate locations to contain an object. This approach will be compared to other state of the art algorithms that extract object candidates for an image. The final goal of this work is to semantically segment images efficiently by exploiting the multiscale information provided by a hierarchical partition, maximizing the accuracy of the segmentation when only a very few regions of the partition are analysed.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
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
INTRA BLOCK AND INTER BLOCK NEIGHBORING JOINT DENSITY BASED APPROACH FOR JPEG...ijsc
Steganalysis is the method used to detect the presence of any hidden message in a cover medium. A novel
approach based on feature mining on the discrete cosine transform (DCT) domain based approach,
machine learning for steganalysis of JPEG images is proposed. The neighboring joint density on both
intra-block and inter-block are extracted from the DCT coefficient array. After the feature space has been
constructed, it uses SVM like binary classifier for training and classification. The performance of the
proposed method on different Steganographic systems named F5, Pixel Value Differencing, Model Based
Steganography with and without deblocking, JPHS, Steghide etc are analyzed. Individually each feature
and combined features classification accuracy is checked and concludes which provides better
classification.
Shallow vs. Deep Image Representations: A Comparative Study with Enhancements...CSCJournals
This document compares shallow and deep image representations for object recognition. It discusses the traditional pipeline approach using handcrafted features extracted via local feature detectors and descriptors, then encoded and pooled. It proposes enhancements to this pipeline by augmenting features. It also discusses end-to-end deep learning models that learn representations directly from images in multiple layers without prior domain knowledge. The purpose is to compare shallow and deep representations, and improve results by combining deep models in an ensemble.
Improved Performance of Unsupervised Method by Renovated K-MeansIJASCSE
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed K-Means algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.
The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, the existing descriptors are either too computationally expensive to achieve real-time performance, or not sufficiently distinctive to identify correct matches from a large database with various transformations. In this paper, we propose a highly efficient and distinctive binary descriptor, called local difference binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pair wise grid cells within the patch. A multiple-gridding strategy and a salient bit-selection method are applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that compared to the existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieving a greater accuracy and faster speed for mobile object recognition and tracking tasks.
The Longest Day Event 2015 - Alzheimer’s AssociationScott Robarge
Scott Robarge founded a recruiting firm in 2010 called Another8 that finds talent for startups. He also contributes to the Alzheimer's Association, which will hold its annual The Longest Day event on June 21st to raise money for Alzheimer's research. Participating teams will do fundraising activities from sunrise to sunset and are asked to raise a minimum of $1,600; past activities have included cycling, bowling, and art shows tailored to interests of those with Alzheimer's.
Ensayo de derecho agrario 2da evaluaciónjm11540042
Este documento resume los conceptos clave del derecho agrario según varios autores y describe los principios constitucionales y legales que lo sustentan. Explica que el derecho agrario regula todo lo relacionado con la producción agrícola, los agricultores, la explotación de la tierra y la protección de los recursos naturales. También identifica a los sujetos involucrados en las relaciones jurídicas agrarias, como productores, no productores, sujetos positivos y negativos.
El documento describe un evento organizado por la Academia de Educación Pure Love en Madrid, España. El evento incluirá presentaciones sobre el fundador del Movimiento de Unificación para la Paz Mundial y su autobiografía, exposiciones de 5 organizaciones y personalidades premiadas por su contribución a la sociedad, una oración y brindis por la paz mundial, y la entrega de premios. El objetivo del evento es apoyar la visión de la Dra. Hak Ja Han Moon de contribuir a la paz mundial en América Latina y España.
Este documento não fornece informações substanciais para resumir. Ele consiste apenas em números e caracteres sem significado. Um resumo precisa ter pelo menos algum conteúdo para poder extrair as ideias e informações essenciais. Infelizmente, este documento não fornece nenhum conteúdo para resumir.
Prot. 2734 15 plc 006-2015 - altera dispositivos na lei complementar nº 019Claudio Figueiredo
PLC altera dispositivos do Estatuto do Magistério Público de Vila Velha (Lei Complementar nº 019/11), permitindo a lotação provisória de professores municipais em unidades administrativas da Secretaria de Educação.
Este documento compara dos procesos a pequeña escala para la licuefacción de gas natural en paquetes skid-mounted. El primer proceso utiliza un ciclo de mezclado-refrigerante sin prerefrigeración de propano, mientras que el segundo proceso utiliza un ciclo de ampliador N2-CH4. Los resultados muestran que el segundo proceso tiene un menor consumo de energía, pero una menor tasa de licuefacción debido a la falta de prerefrigeración en el primer proceso. La gran diferencia de temperatura y la carga de
Teks tersebut membahas tentang diet dan pola makan yang disebut OCD (Obsessive Corbuzier's Diet). OCD adalah metode puasa dimana seseorang tetap bisa makan apa saja namun hanya makan dalam jangka waktu tertentu, misalnya 8 jam sehari. Metode ini dianggap lebih mudah dibandingkan diet konvensional yang rumit aturan makanannya sehingga peluang kepatuhannya lebih besar.
4 herrammientas para nuestra proteccion dcardozoMercedes Marrero
Este documento describe las herramientas necesarias para la protección de la sociedad, incluyendo la capacitación, educación y concientización pública sobre seguridad ciudadana y reducción de riesgos de desastres. También destaca los esfuerzos de la Universidad Central de Venezuela para promover la seguridad a través de brigadas de bomberos, cursos de extensión, y convenios de cooperación. Finalmente, enfatiza que las comunidades requieren herramientas para adoptar enfoques basados en el conocimiento de riesgos y para fortalecer su
Cultura, ciudad y acción colectiva; Eder Silvaesbquilla94
Este documento define los términos cultura, ciudad y acción colectiva. Explica que la cultura se refiere al conjunto de conocimientos y modos de vida de un grupo social. La ciudad es una aglomeración urbana donde predomina la industria y los servicios. La acción colectiva implica la cooperación de un grupo para alcanzar un objetivo común.
$$ Using statistics to search and annotate pictures an evaluation of semantic...mhmt82
This document summarizes and compares two approaches to semantic image annotation and retrieval on large databases: Supervised Multi-class Labeling (SML) and Supervised Category-based Labeling (SCBL). SML treats each semantic concept as a separate image class and learns class-conditional distributions, allowing optimal annotation and retrieval. SCBL groups images into categories, labels categories with concepts, and annotates images with frequent category labels, trading off performance for scalability. The document evaluates these approaches on large databases to establish their relative performance and scalability.
LEARNING TO RANK IMAGE TAGS WITH LIMITED TRAINING EXAMPLES - IEEE PROJECTS I...Nexgen Technology
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NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Learning to rank image tags with limitednexgentech15
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
LEARNING TO RANK IMAGE TAGS WITH LIMITED TRAINING EXAMPLESnexgentechnology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Learning to Rank Image Tags With Limited Training Examples1crore projects
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The document evaluates the bag of features technique for visual object detection. It compares the performance of support vector machines, k-nearest neighbors, and decision trees on 6 object classes using SURF keypoints and SIFT descriptors. SVM achieved the best accuracy rate of 91.9% while decision trees performed worst at 71.1%. The author proposes two enhancements: 1) a hybrid algorithm combining bag of features with geometric constraints or 2) reimplementing the algorithm with a convolutional neural network to incorporate spatial information.
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.
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
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
Semantic annotation of images is an important research topic on both image understanding and databaseor 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 nnotation 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
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.
A Low Rank Mechanism to Detect and Achieve Partially Completed Image TagsIRJET Journal
1. The document proposes a low-rank mechanism to detect and complete partially tagged images by approximating a global nonlinear model with local linear models using locality sensitivity and low-rank factorization.
2. It describes searching images based on category, keywords, or non-similar images and re-ranking images based on user likes/dislikes to increase the rank of more viewed images.
3. The proposed method is evaluated on a dataset showing its effectiveness over previous approaches through improved accuracy.
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.
A Multi Criteria Decision Making Based Approach for Semantic Image Annotation ijcax
Automatic image annotation has emerged as an important research topic due to its potential application on
both image understanding and web image search. This paper presents a model, which integrates visual
topics and regional contexts to automatic image annotation. Regional contexts model the relationship
between the regions, while visual topics provide the global distribution of topics over an image. Previous
image annotation methods neglected the relationship between the regions in an image, while these regions
are exactly explanation of the image semantics, therefore considering the relationship between them are
helpful to annotate the images. Regional contexts and visual topics are learned by PLSA (Probability
Latent Semantic Analysis) from the training data. The proposed model incorporates these two types of
information by MCDM (Multi Criteria Decision Making) approach based on WSM (Weighted Sum
Method). Experiments conducted on the 5k Corel dataset demonstrate the effectiveness of the proposed
model.
A Multi Criteria Decision Making Based Approach for Semantic Image Annotation ijcax
Automatic image annotation has emerged as an important research topic due to its potential application on
both image understanding and web image search. This paper presents a model, which integrates visual
topics and regional contexts to automatic image annotation. Regional contexts model the relationship
between the regions, while visual topics provide the global distribution of topics over an image. Previous
image annotation methods neglected the relationship between the regions in an image, while these regions
are exactly explanation of the image semantics, therefore considering the relationship between them are
helpful to annotate the images. Regional contexts and visual topics are learned by PLSA (Probability
Latent Semantic Analysis) from the training data. The proposed model incorporates these two types of
information by MCDM (Multi Criteria Decision Making) approach based on WSM (Weighted Sum
Method). Experiments conducted on the 5k Corel dataset demonstrate the effectiveness of the proposed
model.
A Multi Criteria Decision Making Based Approach for Semantic Image Annotation ijcax
Automatic image annotation has emerged as an important research topic due to its potential application on
both image understanding and web image search. This paper presents a model, which integrates visual
topics and regional contexts to automatic image annotation. Regional contexts model the relationship
between the regions, while visual topics provide the global distribution of topics over an image. Previous
image annotation methods neglected the relationship between the regions in an image, while these regions
are exactly explanation of the image semantics, therefore considering the relationship between them are
helpful to annotate the images. Regional contexts and visual topics are learned by PLSA (Probability
Latent Semantic Analysis) from the training data. The proposed model incorporates these two types of
information by MCDM (Multi Criteria Decision Making) approach based on WSM (Weighted Sum
Method). Experiments conducted on the 5k Corel dataset demonstrate the effectiveness of the proposed
model.
A Multi Criteria Decision Making Based Approach for Semantic Image Annotation ijcax
Automatic image annotation has emerged as an important research topic due to its potential application on
both image understanding and web image search. This paper presents a model, which integrates visual
topics and regional contexts to automatic image annotation. Regional contexts model the relationship
between the regions, while visual topics provide the global distribution of topics over an image. Previous
image annotation methods neglected the relationship between the regions in an image, while these regions
are exactly explanation of the image semantics, therefore considering the relationship between them are
helpful to annotate the images. Regional contexts and visual topics are learned by PLSA (Probability
Latent Semantic Analysis) from the training data. The proposed model incorporates these two types of
information by MCDM (Multi Criteria Decision Making) approach based on WSM (Weighted Sum
Method). Experiments conducted on the 5k Corel dataset demonstrate the effectiveness of the proposed
model.
A Multi Criteria Decision Making Based Approach for Semantic Image Annotationijcax
Automatic image annotation has emerged as an important research topic due to its potential application on
both image understanding and web image search. This paper presents a model, which integrates visual
topics and regional contexts to automatic image annotation. Regional contexts model the relationship
between the regions, while visual topics provide the global distribution of topics over an image. Previous
image annotation methods neglected the relationship between the regions in an image, while these regions
are exactly explanation of the image semantics, therefore considering the relationship between them are
helpful to annotate the images. Regional contexts and visual topics are learned by PLSA (Probability
Latent Semantic Analysis) from the training data. The proposed model incorporates these two types of
information by MCDM (Multi Criteria Decision Making) approach based on WSM (Weighted Sum
Method). Experiments conducted on the 5k Corel dataset demonstrate the effectiveness of the proposed
model.
Similar to $$ Formulating semantic image annotation as a supervised learning problem (20)
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
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$$ Formulating semantic image annotation as a supervised learning problem
1. Appears in IEEE Conf. on Computer Vision and Pattern Recognition, San Diego, 2005.
Formulating Semantic Image Annotation as a Supervised Learning Problem
Gustavo Carneiro†,∗
Department of Computer Science†
University of British Columbia
Vancouver, BC, Canada
Nuno Vasconcelos∗
Department of Electrical and Computer Engineering∗
University of California, San Diego
San Diego, CA, USA
Abstract
the retrieval of database images based on semantic queries.
Current systems achieve these goals by training a classifier
that automatically labels an image with semantic keywords.
This can be posed as either a problem of supervised or unsupervised learning. The earliest efforts focused on the supervised learning of binary classifiers using a set of training
images with and without the semantic of interest [6, 14].
The classifier was then applied to the image database, and
each image annotated with respect to the presence or absence of the concept. Since each classifier is trained in the
“one vs all” (OVA) mode, we refer to this framework as supervised OVA. More recent efforts have been based on unsupervised learning [1, 2, 4, 5, 7, 8, 9], and strive to solve
the problem in its full generality. The basic idea is to introduce a set of latent variables that encode hidden states of
the world, where each state defines a joint distribution on
the space of semantic keywords and image appearance descriptors (in the form of local features computed over image
neighborhoods). During training, a set of labels is assigned
to each image, the image is segmented into a collection of
regions, and an unsupervised learning algorithm is run over
the entire database to estimate the joint density of words
and visual features. Given a new image to annotate, visual
feature vectors are extracted, the joint probability model is
instantiated with those feature vectors, state variables are
marginalized, and a search for the set of labels that maximize the joint density of text and appearance is carried out.
We refer to this framework as “unsupervised”.
We introduce a new method to automatically annotate
and retrieve images using a vocabulary of image semantics. The novel contributions include a discriminant formulation of the problem, a multiple instance learning solution
that enables the estimation of concept probability distributions without prior image segmentation, and a hierarchical description of the density of each image class that enables very efficient training. Compared to current methods
of image annotation and retrieval, the one now proposed
has significantly smaller time complexity and better recognition performance. Specifically, its recognition complexity is O(CxR), where C is the number of classes (or image
annotations) and R is the number of image regions, while
the best results in the literature have complexity O(TxR),
where T is the number of training images. Since the number
of classes grows substantially slower than that of training
images, the proposed method scales better during training,
and processes test images faster. This is illustrated through
comparisons in terms of complexity, time, and recognition
performance with current state-of-the-art methods.
1. Introduction
Content-based image retrieval, the problem of searching
large image repositories according to their content, has been
the subject of a significant amount of computer vision research in the recent past [13]. While early retrieval architectures were based on the query-by-example paradigm, which
formulates image retrieval as the search for the best database match to a user-provided query image, it was quickly
realized that the design of fully functional retrieval systems
would require support for semantic queries [12]. These are
systems where the database images are annotated with semantic keywords, enabling the user to specify the query
through a natural language description of the visual concepts of interest. This realization, combined with the cost
of manual image labeling, generated significant interest in
the problem of automatically extracting semantic descriptors from images.
The two goals associated with this operation are: a) the
automatic annotation of previously unseen images, and b)
Both formulations have strong advantages and disadvantages. Generally, unsupervised labeling leads to significantly more scalable (in database size and number of concepts of interest) training procedures, places much weaker
demands on the quality of the manual annotations required
to bootstrap learning, and produces a natural ranking of keywords for each new image to annotate. On the other hand,
it does not explicitly treat semantics as image classes and,
therefore, provides little guarantees that the semantic annotations are optimal in a recognition or retrieval sense. That
is, instead of annotations that achieve the smallest probability of retrieval error, it simply produces the ones that have
largest joint likelihood under the assumed mixture model.
In this work we show that it is possible to combine the
1
2. wi which are not explicitly annotated with this concept are
incorrectly assigned to D0 and can compromise the classification accuracy. In this sense, the supervise OVA formulation is not amenable to weak labeling. Furthermore, the
set D0 is likely to be quite large when the vocabulary size
L is large and the training complexity is dominated by the
complexity of learning the conditional density for Y = 0.
Applying (2) to the query image I, produces a sequence
of labels wi ∈ {0, 1}, i ∈ {1, . . . , L}, and a set of posteˆ
rior probabilities PYi |X (1|x) that can be taken as degrees of
confidence on the annotation. Notice, however, that these
are posterior probabilities relative to different classification
problems and do not establish a natural ordering of importance of the keywords wi as descriptors of I. Nevertheless, the binary decision regarding whether each concept is
present in the image or not is a minimum probability of error decision.
advantages of the two formulations through a slight reformulation of the supervised one. This consists of defining an
M -ary classification problem where each of the semantic
concepts of interest defines an image class. At annotation
time, these classes all directly compete for the image to annotate, which no longer faces a sequence of independent
binary tests. This supervised M -ary formulation obviously
retains the classification and retrieval optimality of supervised OVA, but 1) produces a natural ordering of keywords
at annotation time, and 2) eliminates the need to compute a
“non-class” model for each of the semantic concepts of interest. In result, it has learning complexity equivalent to that
of the unsupervised formulation and, like the latter, places
much weaker requirements on the quality of manual labels
than supervised OVA. The method now proposed is compared to the state-of-the-art methods of [5, 8] using the experimental setup introduced in [4]. The results show that the
approach now proposed has advantages not only in terms of
annotation and retrieval accuracy, but also in terms of efficiency.
2.2. Unsupervised Labeling
The basic idea underlying the unsupervised learning formulation [1, 4, 2, 5, 7] is to introduce a variable L that encodes hidden states of the world. Each of these states then
defines a joint distribution for keywords and image features.
The various methods differ in the definition of the states of
the hidden variable: some associate a state to each image in
the database [5, 8], others associate them with image clusters [1, 4, 2]. The overall model is of the form
2. Semantic Labeling
The goal of semantic image labeling is to, given an
image I, extract, from a vocabulary L of semantic descriptors, the set of keywords, or captions, w that best
describes I. Learning is based on a training set D =
{(I1 , w1 ), . . . , (ID , wD )} of image-caption pairs. The
training set is said to be weakly labeled if the absence of
a keyword from wi does not necessarily mean that the associated concept is not present in Ii . This is usually the case
given the subjectivity of the labeling task.
S
PX,W (x, w) =
Let L = {w1 , . . . , wL } be the vocabulary of semantic
labels, or keywords, wi . Under the supervised OVA formulation, labeling is formulated as a collection of L detection
problems that determine the presence/absence of the concepts wi in the image I. Consider the ith such problem and
the random variable Yi such that
1, if I contains concept wi
0, otherwise.
(1)
Given a collection of image features X extracted from I,
the goal is to infer the state of Yi with smallest probability of
error, for all i. This can be solved by application of standard
Bayesian decision theory, namely by declaring the concept
as present if
PX|Yi (x|1)PYi (1) ≥ PX|Yi (x|0)PYi (0)
(3)
where S is the number of possible states of L, X the set
of feature vectors extracted from I and W the vector of
keywords associated with this image. Since this is a mixture model, learning is usually based on the expectationmaximization (EM) [3] algorithm, but the details depend on
the particular definition of hidden variable and probabilistic
model adopted for PX,W (x, w).
The simplest model in this family [5, 8], which has also
achieved the best results in experimental trials, makes each
image in the training database a state of the latent variable,
and assumes conditional independence between image features and keywords, i.e.
2.1. Supervised OVA Labeling
Yi =
PX,W|L (x, w|l)PL (l)
l=1
D
PX,W (x, w) =
PX|L (x|l)PW|L (w|l)PL (l)
(4)
l=1
where D is the training set size. This enables individual estimation of PX|L (x|l) and PW|L (w|l), as is common in the
probabilistic retrieval literature [13], therefore eliminating
the need to iterate the EM algorithm over the entire database (a procedure of large computational complexity). In
this way, the training complexity is equivalent to that of
learning the conditional densities for Yi = 1 in the supervised OVA formulation. This is significantly smaller than
the learning complexity of that formulation (which, as discussed above, is dominated by the much more demanding
(2)
where PX|Yi (x|j) is the class-conditional density and
PYi (j) the prior probability for class j ∈ {0, 1}.
Training consists of assembling a training set D1 containing all images labeled with the concept wi , a training
set D0 containing the remaining images, and using some
density estimation procedure to estimate PX|Yi (x|j) from
Dj , j ∈ {0, 1}. Note that any images containing concept
2
3. semantic densities PX|W (x|i) with computation equivalent
to that required to estimate one density per image. Hence,
the supervised M -ary formulation has learning complexity
equivalent to the simpler of the unsupervised labeling approaches (4).
Second, the ith semantic class density is estimated from
a training set Di containing all feature vectors extracted
from images labeled with concept wi . While this will be
most accurate if all images that contain the concept include
wi in their captions, images for which this keyword is missing will simply not be considered. If the number of images
correctly annotated is large, this is likely not to make any
practical difference. If that number is small, missing labeled images can always be compensated for by adopting
Bayesian (regularized) estimates. In this sense, the supervised M -ary formulation is equivalent to the unsupervised
formulation and, unlike the supervised OVA formulation,
not severely affected by weak labeling.
Finally, at annotation time, the supervised M -ary formulation provides a natural ordering of the semantic classes,
by the posterior probability PW |X (w|x). Unlike the OVA
case, under the M -ary formulation these posteriors are relative to the same classification problem, a problem where
the semantic classes compete to explain the query. This ordering is, in fact, equivalent to that adopted by the unsupervised learning formulation (5), but now leads to a Bayesian
decision rule that is matched to the class structure of the underlying generative model. Hence, this concept ordering is
optimal in a minimum probability of error sense.
task of learning the conditionals for Yi = 0). The training of the PW|L (w|l), l ∈ {1, . . . , D} consists of a maximum likelihood estimate based on the annotations associated with the lth training image, and usually reduces to
counting [5, 8]. Note that, while the quality of the estimates
improves when the image is annotated with all concepts that
it includes, it is possible to compensate for missing labels
by using standard Bayesian (regularized) estimates [5, 8].
Hence, the impact of weak labeling is not major under this
formulation.
At annotation time, the feature vectors extracted from
the query I are used in (3) to obtain a function of w that
provides a natural ordering of the relevance of all possible
captions for the query. This function can be the joint density
of (3) or the posterior density
PX,W (x, w)
.
(5)
PX (x)
Note that, while this can be interpreted as the Bayesian decision rule for a classification problem with the states of
W as classes, such class structure is not consistent with the
generative model of (3) which enforces a causal relationship from L to W. This leads to a very weak dependency
between the observation X and class W variables, e.g. that
they are independent given L in the model of (4). Therefore,
in our view, this formulation imposes a mismatch between
the class structure used for the purposes of designing the
probabilistic models (where the states of the hidden variable
are the dominant classes) and that used for labeling (which
assume the states of W to be the real classes). This implies
that the annotation decisions are not optimal in a minimum
probability of error sense.
PW|X (w|x) =
4. Estimation of Semantic Class Distributions
Given the collection of semantic class-conditional densities PW |X (w|x), supervised M -ary labeling is relatively
trivial (it consists of a search for the solution of (6)). Two
interesting questions arise, however, in the context of density estimation. Hereafter, assume that x consists of a feature vector extracted from an image region of small spatial
support.
3. Supervised M-ary Labeling
The supervised M -ary formulation now proposed explicitly makes the elements of the semantic vocabulary the
classes of the M -ary classification problem. That is, by introducing 1) a random variable W , which takes values in
{1, . . . , L}, so that W = i if and only if x is a sample from
the concept wi , and 2) a set of class-conditional distributions PX|W (x|i), i ∈ {1, . . . , L} for the distribution visual
features given the semantic class. Similarly to supervised
OVA, the goal is to infer the state of W with smallest probability of error. Given a set of features x from a query image
I this is accomplished by application of the Bayes decision
rule
i∗ = arg max PX|W (x|i)PW (i)
(6)
4.1. Modeling Classes Without Segmentation
So far, we have assumed that all samples in the training set Di are from concept wi . In practice, however, this
would require careful segmentation and labeling of all training images. While concepts such as “Indoor”, “Outdoor”,
“Coastline”, or “Landscape” tend to be holistic (i.e. the entire image is, or is not, in the class), most concepts refer to
objects and other items that only cover a part of any image
(e.g. “Bear”, “Flag”, etc.). Hence, most images contain a
combination of various concepts. The creation of a training set Di of feature vectors exclusively drawn from the ith
class would require manual segmentation of all training images, followed by labeling of the individual segments.
Since this is unfeasible, an interesting question is
whether it is possible to estimate the class-conditional density from a training set composed of images with a signifi-
i
where PW (i) is a prior probability for the ith semantic
class. The difference with respect to the OVA formulation is
that instead of a sequence of L binary detection problems,
we now have a single M -ary problem with L classes.
This has several advantages. First, there is no longer a
need to estimate L non-class distributions (Yi = 0 in (1)),
an operation which, as discussed above, is the computational bottleneck of the OVA formulation. On the contrary,
as will be shown in Section 4, it is possible to estimate all
3
4. cant percentage of feature vectors drawn from other classes.
The answer to this question is affirmative, it is the basis of
so-called multiple instance learning [10], where each image
is labeled positive with respect to a specific concept w if at
least one of its regions R is an exemplar of this concept,
and labeled negative otherwise. While no explicit correspondence between regions and concepts is included in the
training set, it is still possible to learn a probability distribution for a concept by exploiting the consistent appearance of
samples from this distribution in all images of the concept.
To see this, let Rw be the region of the feature space populated by the concept w that appears in all positive images.
Assume, further, that the remaining samples are uniformly
distributed. Since the probability of this uniform component must integrate to one, it must necessarily have small
amplitude. Hence, the probability density of the image ensemble is dominated by the probability mass in Rw . As the
number of images goes to infinity, this property holds independently of how small the probability mass in Rw is for
each image.
of the feature space. For example, if all densities are histograms defined on a partition of the feature space X into
Q cells {Xq }, q = 1, · · · , Q, and hq the number of feature
i,j
vectors from class i that land on cell Xq for image j, then
the average class histogram is simply
1
ˆ
hq =
i
D
hq .
i,j
j
However, when 1) the underlying partition is not the same
for all histograms or 2) more sophisticated models (e.g.
mixture or non-parametric density estimates) are used,
model averaging is not as simple.
Naive Averaging: consider, for example, the Gauss mixture model
k
πi,l G(x, µk , Σk ),
i,l
i,l
PX|L,W (x|l, i) =
(8)
k
k
where πi,l is a probability mass function such that
k
k πi,l = 1. Direct application of (7) leads to
PX|W (x|i) =
4.2. Density Estimation
1
D
k
πi,l G(x, µk , Σk )
i,l
i,l
(9)
k,l
i.e. a D-fold increase in the number of Gaussian components per mixture. Since, at annotation time, this probability
has to be evaluated for each semantic class, it is clear that
straightforward model averaging will lead to an extremely
slow annotation process.
Mixture hierarchies: one efficient alternative to the
complexity of model averaging is to adopt a hierarchical
density estimation method first proposed in [15] for image indexing. This method is based on a mixture hierarchy
where children densities consist of different combinations
of subsets of the parents components. A formal definition
is given in [15], we omit the details for brevity. The important point is that, when the densities conform to the mixture
hierarchy model, it is possible to estimate the parameters of
the class mixture directly from those available for the individual image mixtures, using a two-stage procedure. The
first stage, is the naive averaging of (9). Assuming that each
mixture has K components, this leads to an overall mixture
with DK components of parameters
Given the training set Di of images containing concept
wi , the estimation of the density PX|W (x|i) can proceed
in four different ways: direct estimation, model averaging,
naive averaging, hierarchical estimation.
Direct Estimation: direct estimation consists of estimating the class density from a training set containing all feature vectors from all images in D. The main disadvantage
of this strategy is that, for classes with a sizable number of
images, the training set is likely to be quite large. This creates a number of practical problems, e.g. the requirement
for large amounts of memory, and makes sophisticated density estimation techniques unfeasible. One solution is to
discard part of the data, but this is suboptimal in the sense
that important training cases may be lost.
Model Averaging: model averaging performs the estimation of PX|W (x|i) in two steps. In the first step, a density
estimate is produced for each image, originating a sequence
PX|L,W (x|l, i), l ∈ {1, . . . D} where L is a hidden variable
that indicates the image number. The class density is then
obtained by averaging the densities in this sequence
1
PX|W (x|i) =
PX|L,W (x|l, i).
(7)
D
k
{πj , µk , Σk }, j = 1, . . . , D, k = 1, . . . , K.
j
j
(10)
The second is an extension of the EM algorithm, which
clusters the Gaussian components into a T -component mixture, where T is the number of components at the class
t
level. Denoting by {πc , µt , Σt }, t = 1, . . . , T the paramec
c
ters of the class mixture, this algorithm iterates between the
following steps.
E-step: compute
l
Note that this is equivalent to the density estimate obtained
under the unsupervised labeling framework, if the text component of the joint density of (3) is marginalized and the
hidden states are images (as is the case of (4)). The main
difference is that, while under M -ary supervised labeling
the averaging is done only over the set of images that belong to the semantic class, under unsupervised labeling it is
done over the entire database. This, once again, reflects the
lack of classification optimality of the later formulation.
The direct application of (7) is feasible when the densities PX|L,W (x|l, i) are defined over a (common) partition
t −1
1
ht
jk
=
G(µk , µt , Σt )e− 2 trace{(Σc )
c
c
j
1
l
4
Σk }
j
k
πj N
l −1 Σk }
j
G(µk , µl , Σl )e− 2 trace{(Σc )
c
c
j
t
πc
k
πj N
,
l
πc
(11)
5. (µt )new =
c
t
t
wjk µk , where wjk =
j
jk
jk
(Σt )new
c
t
wjk
=
k
ht πj
jk
Σk
j
+
(µk
j
−
µt )(µk
c
j
k
ht πj
jk
−
µt )T
c
(13)
Mean Per−word Recall
Mean Per−word Precision
50
0.2
0.1
19
0
n
r
t
e
nc atio CRM −rec BRM DCT −Hie
rre sl
M
M ct− Mix
cu Tran
CR
oc
−re
−
M
Co
CR
C
0.29
0.23
0.3
49
0.25
0.24
100
0.22
0.21
0.4
107
0.23
0.22
107
122
0.19
0.16
(12)
PK
0.5
137
119
0.04
0.06
=
ht
jk
# words with recall > 0
t
(πc )new
jk
150
0.02
0.03
where N is a user-defined parameter (see [15] for details).
M-step: set
0
t
e tion
T
ier
M
ec
nc
RM DC −H
CR M−r
rre nsla
MB ect− Mix
ccu Tra
CR
−r
M
CR
o
o−
(14)
.
Figure 1. Performance comparison of automatic annota-
jk
tion on the Corel dataset.
Notice that the number of parameters in each image mixture is orders of magnitude smaller than the number of feature vectors in the image itself. Hence the complexity of
estimating the class mixture parameters is negligible when
compared to that of estimating the individual mixture parameters for all images in the class. It follows that the overall
training complexity is dominated by the latter task, i.e. only
marginally superior to that of naive averaging and significantly smaller than that associated with direct estimation
of class densities. On the other hand, the complexity of
evaluating likelihoods is exactly the same as that achievable
with direct estimation, and significantly smaller than that of
naive averaging.
One final interesting property of the EM steps above
is that they enforce a data-driven form of regularization
which improves generalization. This regularization is visible in (14) where the variances on the left hand-size can
never be smaller than those on the right-hand side. We have
observed that, due to this property, hierarchical class density estimates are much more reliable than those obtained
with direct learning.
5.1. Automatic Image Annotation
We start by assessing performance on the task of automatic image annotation. Given an un-annotated image, the
task is to automatically generate a caption which is then
compared to the annotation made by a human. Similarly to
[8, 5] we define the automatic annotation to consist of the
five classes under which the image has largest likelihood.
We then compute the recall and precision of every word in
the test set. Given a particular semantic descriptor w, if
there are |wH | human annotated images with the descriptor
w in the test set, and the system annotates |wauto | images
with that descriptor, where |wC | are correct, recall and pre|wC
|wC
cision are given by recall = |wH | , precision = |wauto|| .
|
Fig. 1 shows the results obtained on the complete set of
260 words that appear in the test set. The values of recall
and precision were averaged over the set of testing words,
as suggested by [8, 5]. Also presented are results (borrowed
from [8, 5]) obtained with various other methods under this
same experimental setting. Specifically, we consider: the
Co-occurrence Model [11], the Translation Model [4],The
Continuous-space Relevance Model (CRM-rect)[8, 5], and
the Multiple-Bernoulli Relevance Model (MBRM) [5]. The
method now proposed is denoted by ’Mix-Hier’. We also
implemented the CRM-rect using the 8 × 8 DCT features,
which is denoted as ’CRM-rect-DCT’.
Overall, the method now proposed achieves the best performance. When compared to the previous best results
(MBRM) it exhibits a gain of 16% in recall for an equivalent level of precision. Similarly, the number of words with
positive recall increases by 15%. It is also worth noting that
the CRM-rect model with DCT features, performs slightly
worse than the original CRM-rect. This indicates that the
performance of Mix-Hier may improve with a better set of
features. We intent to investigate this in the future.
Another important issue is the complexity of the annotation process. The complexity of CRM-rectangles and
MBRM is O(T R), where T is the number of training images and R is the number of image regions. Compared to
those methods, Mix-Hier has a significantly smaller time
complexity of O(CR), where C is the number of classes
(or image annotations). Assuming a fixed number of regions R,Fig. 2 shows how the annotation time of a test image grows for Mix-Hier and MBRM, as a function of the
5. Experimental Results
In this section, we present experimental results on a data
set, Corel, that has been continuously adopted as a standard
way to assess annotation and retrieval performance [4, 8, 5].
The Translation Model of [4] was the first milestone in the
area of semantic annotation, in the sense of demonstrating results of practical interest. After various years of research, and several other contributions, the best existing
results are, to our knowledge, those presented in [5]. We
therefore adopt an evaluation strategy identical to that used
in this work. The data set used in all experiments consists
of 5, 000 images from 50 Corel Stock Photo CDs, and was
divided into two parts: a training set of 4, 500 images and
a test set of 500 images. Each CD includes 100 images of
the same topic, and each image is associated with 1-5 keywords. Overall there are 371 keywords in the dataset. In
all cases, the YBR color space was adopted, and the image
features were coefficients of the 8 × 8 discrete cosine transform (DCT). Note that this is a feature set different that that
used in [4, 8, 5], which consists of color, texture, and shape
features.
5
6. Image
Annotation
Human: bear,polar,
snow,tundra
Mix-Hier: polar,tundra,
bear,snow,ice
Human: water,beach
people,sunset
Mix-Hier: sunset,sun,
palm,clouds,sea
Figure 2. Time complexity for annotating a test image of
Human: buildings,clothes,
shops,streets
Mix-Hier: buildings,street,
shops,people,skyline
the Corel data set.
Table 1. Retrieval results on Corel.
Mean Average Precision for the Corel Dataset
Models
All 260 words Words with recall > 0
Mix-Hier
0.31
0.49
MBRM
0.30
0.35
Figure 3. Comparisons of annotations made by our system and annotations made by a Human subject.
number of training images. In our experiments, over the
set of 500 test images, the average annotation time was 268
seconds for Mix-Hier, and 371 seconds for CRM-rect-DCT.
5.2. Image Retrieval with Single Word Queries
Figure 4. First five ranked results for the queries ’tiger’
In this section we analyze the performance of semantic retrieval. In this case, the precision and recall measures
are computed as follows. If the n most similar images to a
query are retrieved, recall is the percentage of all relevant
images that are contained in that set and precision the percentage of the n which are relevant (where relevant means
that the ground-truth annotation of the image contains the
query descriptor). Once again, we adopted the experimental setup of [5]. Under this set-up, retrieval performance
is evaluated by the mean average precision. As can be sen
from Table 1, for ranked retrieval on Corel, Mix-Hier produces results superior to those of MBRM. In particular, it
achieves a gain of 40% mean average precision on the set
of words that have positive recall.
(first row) and ’mountain’ (second row) in the Corel data
set using our retrieval system.
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5.3. Results: Examples
In this section we present some examples of the annotations produced by our system. Fig. 3 illustrates the fact
that, as reported in Table 1, Mix-Hier has a high level of recall. Frequently, when the system annotates an image with
a descriptor not contained in the human-made caption, this
annotation is not necessarily wrong. Finally, Fig. 4 illustrates the performance of the system on one word queries.
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