The Experts below are selected from a list of 5787 Experts worldwide ranked by ideXlab platform
Lei Wang - One of the best experts on this subject based on the ideXlab platform.
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A Hierarchical Word-Merging Algorithm with Class Separability Measure
IEEE transactions on pattern analysis and machine intelligence, 2014Co-Authors: Lei Wang, Luping Zhou, Chunhua Shen, Lingqiao Liu, Huan LiuAbstract:In image recognition with the bag-of-features model, a small-sized visual codebook is usually preferred to obtain a low-dimensional histogram representation and high computational efficiency. Such a visual codebook has to be discriminative enough to achieve excellent recognition performance. To create a compact and discriminative codebook, in this paper we propose to merge the visual words in a large-sized initial codebook by maximally preserving Class Separability. We first show that this results in a difficult optimization problem. To deal with this situation, we devise a suboptimal but very efficient hierarchical word-merging algorithm, which optimally merges two words at each level of the hierarchy. By exploiting the characteristics of the Class Separability measure and designing a novel indexing structure, the proposed algorithm can hierarchically merge 10,000 visual words down to two words in merely 90 seconds. Also, to show the properties of the proposed algorithm and reveal its advantages, we conduct detailed theoretical analysis to compare it with another hierarchical word-merging algorithm that maximally preserves mutual information, obtaining interesting findings. Experimental studies are conducted to verify the effectiveness of the proposed algorithm on multiple benchmark data sets. As shown, it can efficiently produce more compact and discriminative codebooks than the state-of-the-art hierarchical word-merging algorithms, especially when the size of the codebook is significantly reduced.
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on the optimality of sequential forward feature selection using Class Separability measure
Digital Image Computing: Techniques and Applications, 2011Co-Authors: Lei Wang, Chunhua Shen, Richard HartleyAbstract:This paper studies sequential forward feature selection that uses the scatter-matrix-based Class Separability measure. We find that by adding a scale factor to each iteration of the conventional sequential selection, a sequential selection that guarantees the global optimum can be attained. We give a thorough theoretical proof of its optimality via a novel geometric interpretation, and this leads to a unified framework including the optimal sequential selection, the conventional sequential selection and the best-individual-N selection. In addition, we show that with our formulation, feature selection can be treated as a linear fractional maximization problem, and it can be efficiently solved by algorithms well developed in the literature. This gives a non-sequential globally optimal feature selection algorithm. Both theoretical and experimental study demonstrate their efficiency.
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DICTA - On the Optimality of Sequential Forward Feature Selection Using Class Separability Measure
2011 International Conference on Digital Image Computing: Techniques and Applications, 2011Co-Authors: Lei Wang, Chunhua Shen, Richard HartleyAbstract:This paper studies sequential forward feature selection that uses the scatter-matrix-based Class Separability measure. We find that by adding a scale factor to each iteration of the conventional sequential selection, a sequential selection that guarantees the global optimum can be attained. We give a thorough theoretical proof of its optimality via a novel geometric interpretation, and this leads to a unified framework including the optimal sequential selection, the conventional sequential selection and the best-individual-N selection. In addition, we show that with our formulation, feature selection can be treated as a linear fractional maximization problem, and it can be efficiently solved by algorithms well developed in the literature. This gives a non-sequential globally optimal feature selection algorithm. Both theoretical and experimental study demonstrate their efficiency.
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Feature Selection With Redundancy-Constrained Class Separability
IEEE transactions on neural networks, 2010Co-Authors: Luping Zhou, Lei Wang, Chunhua ShenAbstract:Scatter-matrix-based Class Separability is a simple and efficient feature selection criterion in the literature. However, the conventional trace-based formulation does not take feature redundancy into account and is prone to selecting a set of discriminative but mutually redundant features. In this brief, we first theoretically prove that in the context of this trace-based criterion the existence of sufficiently correlated features can always prevent selecting the optimal feature set. Then, on top of this criterion, we propose the redundancy-constrained feature selection (RCFS). To ensure the algorithm's efficiency and scalability, we study the characteristic of the constraints with which the resulted constrained 0-1 optimization can be efficiently and globally solved. By using the totally unimodular (TUM) concept in integer programming, a necessary condition for such constraints is derived. This condition reveals an interesting special case in which qualified redundancy constraints can be conveniently generated via a clustering of features. We study this special case and develop an efficient feature selection approach based on Dinkelbach's algorithm. Experiments on benchmark data sets demonstrate the superior performance of our approach to those without redundancy constraints.
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Feature Selection with Kernel Class Separability
IEEE transactions on pattern analysis and machine intelligence, 2008Co-Authors: Lei WangAbstract:Classification can often benefit from efficient feature selection. However, the presence of linearly nonseparable data, quick response requirement, small sample problem and noisy features makes the feature selection quite challenging. In this work, a Class Separability criterion is developed in a high-dimensional kernel space, and feature selection is performed by the maximization of this criterion. To make this feature selection approach work, the issues of automatic kernel parameter tuning, the numerical stability, and the regularization for multi-parameter optimization are addressed. Theoretical analysis uncovers the relationship of this criterion to the radius-margin bound of the SVMs, the KFDA, and the kernel alignment criterion, providing more insight on using this criterion for feature selection. This criterion is applied to a variety of selection modes with different search strategies. Extensive experimental study demonstrates its efficiency in delivering fast and robust feature selection.
Chunhua Shen - One of the best experts on this subject based on the ideXlab platform.
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A Hierarchical Word-Merging Algorithm with Class Separability Measure
IEEE transactions on pattern analysis and machine intelligence, 2014Co-Authors: Lei Wang, Luping Zhou, Chunhua Shen, Lingqiao Liu, Huan LiuAbstract:In image recognition with the bag-of-features model, a small-sized visual codebook is usually preferred to obtain a low-dimensional histogram representation and high computational efficiency. Such a visual codebook has to be discriminative enough to achieve excellent recognition performance. To create a compact and discriminative codebook, in this paper we propose to merge the visual words in a large-sized initial codebook by maximally preserving Class Separability. We first show that this results in a difficult optimization problem. To deal with this situation, we devise a suboptimal but very efficient hierarchical word-merging algorithm, which optimally merges two words at each level of the hierarchy. By exploiting the characteristics of the Class Separability measure and designing a novel indexing structure, the proposed algorithm can hierarchically merge 10,000 visual words down to two words in merely 90 seconds. Also, to show the properties of the proposed algorithm and reveal its advantages, we conduct detailed theoretical analysis to compare it with another hierarchical word-merging algorithm that maximally preserves mutual information, obtaining interesting findings. Experimental studies are conducted to verify the effectiveness of the proposed algorithm on multiple benchmark data sets. As shown, it can efficiently produce more compact and discriminative codebooks than the state-of-the-art hierarchical word-merging algorithms, especially when the size of the codebook is significantly reduced.
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on the optimality of sequential forward feature selection using Class Separability measure
Digital Image Computing: Techniques and Applications, 2011Co-Authors: Lei Wang, Chunhua Shen, Richard HartleyAbstract:This paper studies sequential forward feature selection that uses the scatter-matrix-based Class Separability measure. We find that by adding a scale factor to each iteration of the conventional sequential selection, a sequential selection that guarantees the global optimum can be attained. We give a thorough theoretical proof of its optimality via a novel geometric interpretation, and this leads to a unified framework including the optimal sequential selection, the conventional sequential selection and the best-individual-N selection. In addition, we show that with our formulation, feature selection can be treated as a linear fractional maximization problem, and it can be efficiently solved by algorithms well developed in the literature. This gives a non-sequential globally optimal feature selection algorithm. Both theoretical and experimental study demonstrate their efficiency.
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DICTA - On the Optimality of Sequential Forward Feature Selection Using Class Separability Measure
2011 International Conference on Digital Image Computing: Techniques and Applications, 2011Co-Authors: Lei Wang, Chunhua Shen, Richard HartleyAbstract:This paper studies sequential forward feature selection that uses the scatter-matrix-based Class Separability measure. We find that by adding a scale factor to each iteration of the conventional sequential selection, a sequential selection that guarantees the global optimum can be attained. We give a thorough theoretical proof of its optimality via a novel geometric interpretation, and this leads to a unified framework including the optimal sequential selection, the conventional sequential selection and the best-individual-N selection. In addition, we show that with our formulation, feature selection can be treated as a linear fractional maximization problem, and it can be efficiently solved by algorithms well developed in the literature. This gives a non-sequential globally optimal feature selection algorithm. Both theoretical and experimental study demonstrate their efficiency.
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Feature Selection With Redundancy-Constrained Class Separability
IEEE transactions on neural networks, 2010Co-Authors: Luping Zhou, Lei Wang, Chunhua ShenAbstract:Scatter-matrix-based Class Separability is a simple and efficient feature selection criterion in the literature. However, the conventional trace-based formulation does not take feature redundancy into account and is prone to selecting a set of discriminative but mutually redundant features. In this brief, we first theoretically prove that in the context of this trace-based criterion the existence of sufficiently correlated features can always prevent selecting the optimal feature set. Then, on top of this criterion, we propose the redundancy-constrained feature selection (RCFS). To ensure the algorithm's efficiency and scalability, we study the characteristic of the constraints with which the resulted constrained 0-1 optimization can be efficiently and globally solved. By using the totally unimodular (TUM) concept in integer programming, a necessary condition for such constraints is derived. This condition reveals an interesting special case in which qualified redundancy constraints can be conveniently generated via a clustering of features. We study this special case and develop an efficient feature selection approach based on Dinkelbach's algorithm. Experiments on benchmark data sets demonstrate the superior performance of our approach to those without redundancy constraints.
Manfred Glesner - One of the best experts on this subject based on the ideXlab platform.
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systematic methods for multivariate data visualization and numerical assessment of Class Separability and overlap in automated visual industrial quality control
British Machine Vision Conference, 1994Co-Authors: Andreas Konig, Olaf Bulmahn, Manfred GlesnerAbstract:The focus of this work is on systematic methods for the visualization and quality assessment with regard to Classificatio n of multivariate data sets. Our novel methods and criteria give in visual and numerical form rapid insight in the principal data distribution, the degree of compactness and overlap of Class regions and Class Separability, as well as information to identify outliers in the data set and trace them back to data acquisition. Assessment by visualization and numerical criteria can be exploited for interactive or automatic optimization of feature generation and selection/extraction in pattern recognition problems. Further, we provide a novel criterion to assess the credibility and reliability of the visualization obtained from high dimensional data projection. Our methods will be demonstrated using data from visual industrial quality control and mechatronic applications.
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BMVC - Systematic Methods for Multivariate Data Visualization and Numerical Assessment of Class Separability and Overlap in Automated Visual Industrial Quality Control
Procedings of the British Machine Vision Conference 1994, 1994Co-Authors: Andreas Konig, Olaf Bulmahn, Manfred GlesnerAbstract:The focus of this work is on systematic methods for the visualization and quality assessment with regard to Classificatio n of multivariate data sets. Our novel methods and criteria give in visual and numerical form rapid insight in the principal data distribution, the degree of compactness and overlap of Class regions and Class Separability, as well as information to identify outliers in the data set and trace them back to data acquisition. Assessment by visualization and numerical criteria can be exploited for interactive or automatic optimization of feature generation and selection/extraction in pattern recognition problems. Further, we provide a novel criterion to assess the credibility and reliability of the visualization obtained from high dimensional data projection. Our methods will be demonstrated using data from visual industrial quality control and mechatronic applications.
Richard Hartley - One of the best experts on this subject based on the ideXlab platform.
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on the optimality of sequential forward feature selection using Class Separability measure
Digital Image Computing: Techniques and Applications, 2011Co-Authors: Lei Wang, Chunhua Shen, Richard HartleyAbstract:This paper studies sequential forward feature selection that uses the scatter-matrix-based Class Separability measure. We find that by adding a scale factor to each iteration of the conventional sequential selection, a sequential selection that guarantees the global optimum can be attained. We give a thorough theoretical proof of its optimality via a novel geometric interpretation, and this leads to a unified framework including the optimal sequential selection, the conventional sequential selection and the best-individual-N selection. In addition, we show that with our formulation, feature selection can be treated as a linear fractional maximization problem, and it can be efficiently solved by algorithms well developed in the literature. This gives a non-sequential globally optimal feature selection algorithm. Both theoretical and experimental study demonstrate their efficiency.
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DICTA - On the Optimality of Sequential Forward Feature Selection Using Class Separability Measure
2011 International Conference on Digital Image Computing: Techniques and Applications, 2011Co-Authors: Lei Wang, Chunhua Shen, Richard HartleyAbstract:This paper studies sequential forward feature selection that uses the scatter-matrix-based Class Separability measure. We find that by adding a scale factor to each iteration of the conventional sequential selection, a sequential selection that guarantees the global optimum can be attained. We give a thorough theoretical proof of its optimality via a novel geometric interpretation, and this leads to a unified framework including the optimal sequential selection, the conventional sequential selection and the best-individual-N selection. In addition, we show that with our formulation, feature selection can be treated as a linear fractional maximization problem, and it can be efficiently solved by algorithms well developed in the literature. This gives a non-sequential globally optimal feature selection algorithm. Both theoretical and experimental study demonstrate their efficiency.
Andreas Konig - One of the best experts on this subject based on the ideXlab platform.
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systematic methods for multivariate data visualization and numerical assessment of Class Separability and overlap in automated visual industrial quality control
British Machine Vision Conference, 1994Co-Authors: Andreas Konig, Olaf Bulmahn, Manfred GlesnerAbstract:The focus of this work is on systematic methods for the visualization and quality assessment with regard to Classificatio n of multivariate data sets. Our novel methods and criteria give in visual and numerical form rapid insight in the principal data distribution, the degree of compactness and overlap of Class regions and Class Separability, as well as information to identify outliers in the data set and trace them back to data acquisition. Assessment by visualization and numerical criteria can be exploited for interactive or automatic optimization of feature generation and selection/extraction in pattern recognition problems. Further, we provide a novel criterion to assess the credibility and reliability of the visualization obtained from high dimensional data projection. Our methods will be demonstrated using data from visual industrial quality control and mechatronic applications.
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BMVC - Systematic Methods for Multivariate Data Visualization and Numerical Assessment of Class Separability and Overlap in Automated Visual Industrial Quality Control
Procedings of the British Machine Vision Conference 1994, 1994Co-Authors: Andreas Konig, Olaf Bulmahn, Manfred GlesnerAbstract:The focus of this work is on systematic methods for the visualization and quality assessment with regard to Classificatio n of multivariate data sets. Our novel methods and criteria give in visual and numerical form rapid insight in the principal data distribution, the degree of compactness and overlap of Class regions and Class Separability, as well as information to identify outliers in the data set and trace them back to data acquisition. Assessment by visualization and numerical criteria can be exploited for interactive or automatic optimization of feature generation and selection/extraction in pattern recognition problems. Further, we provide a novel criterion to assess the credibility and reliability of the visualization obtained from high dimensional data projection. Our methods will be demonstrated using data from visual industrial quality control and mechatronic applications.