The Experts below are selected from a list of 6462 Experts worldwide ranked by ideXlab platform
Fanzizhu - One of the best experts on this subject based on the ideXlab platform.
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Supervised Sparse representation method with a heuristic strategy and face recognition experiments
Neurocomputing, 2012Co-Authors: Xuyong, Zuowangmeng, FanzizhuAbstract:In this paper we propose a supervised Sparse representation method for face recognition. We assume that the test sample could be approximately represented by a Sparse Linear Combination of all the ...
Bruno Torrésani - One of the best experts on this subject based on the ideXlab platform.
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Random models for Sparse signals expansion on unions of bases with application to audio signals
IEEE Transactions on Signal Processing, 2008Co-Authors: Matthieu Kowalski, Bruno TorrésaniAbstract:A new approach for signal expansion with respect to hybrid dictionaries, based upon probabilistic modeling is proposed and studied, with emphasis on audio signal processing applications. The signal is modeled as a Sparse Linear Combination of waveforms, taken from the union of two orthonormal bases, with random coefficients. The behavior of the analysis coefficients, namely inner products of the signal with all basis functions, is studied in details, which shows that these coefficients may generally be classified in two categories: significant coefficients versus unsignificant coefficients. Conditions ensuring the feasibility of such a classification are given. When the classification is possible, it leads to efficient estimation algorithms, that may in turn be used for de-noising or coding purpose. The proposed approach is illustrated by numerical experiments on audio signals, using MDCT bases.
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Random Models for Sparse Signals Expansion on Unions of Bases With Application to Audio Signals
IEEE Transactions on Signal Processing, 2008Co-Authors: Matthieu Kowalski, Bruno TorrésaniAbstract:A new approach for signal expansion with respect to hybrid dictionaries, based upon probabilistic modeling is proposed and studied. The signal is modeled as a Sparse Linear Combination of waveforms, taken from the union of two orthonormal bases, with random coefficients. The behavior of the analysis coefficients, namely inner products of the signal with all basis functions, is studied in details, which shows that these coefficients may generally be classified in two categories: significant coefficients versus insignificant coefficients. Conditions ensuring the feasibility of such a classification are given. When the classification is possible, it leads to efficient estimation algorithms, that may in turn be used for denoising or coding purposes. The proposed approach is illustrated by numerical experiments on audio signals, using MDCT bases. However, it is general enough to be applied without much modifications in different contexts, for example in image processing.
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Random models for audio signals expansion on hybrid MDCT dictionaries
2007Co-Authors: Matthieu Kowalski, Bruno TorrésaniAbstract:A new approach for signal expansion with respect to hybrid dictionaries, based upon probabilistic modeling is proposed and studied, with emphasis on audio signal processing applications. The signal is modeled as a Sparse Linear Combination of waveforms, taken from the union of two orthonormal bases, with random coefficients. The behavior of the analysis coefficients, namely inner products of the signal with all basis functions, is studied in details, which shows that these coefficients may generally be classified in two categories: significant coefficients versus unsignificant coefficients. Conditions ensuring the feasibility of such a classification are given. When the classification is possible, it leads to efficient estimation algorithms, that may in turn be used for de-noising or coding purpose. The proposed approach is illustrated by numerical experiments on audio signals, using MDCT bases.
Sandrine Anthoine - One of the best experts on this subject based on the ideXlab platform.
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Recovery and convergence rate of the Frank-Wolfe Algorithm for the m-EXACT-Sparse Problem
IEEE Transactions on Information Theory, 2019Co-Authors: Farah Cherfaoui, Valentin Emiya, Liva Ralaivola, Sandrine AnthoineAbstract:We study the properties of the Frank-Wolfe algorithm to solve the m-EXACT-Sparse reconstruction problem, where a signal y must be expressed as a Sparse Linear Combination of a predefined set of atoms, called dictionary. We prove that when the signal is Sparse enough with respect to the coherence of the dictionary, then the iterative process implemented by the Frank-Wolfe algorithm only recruits atoms from the support of the signal, that is the smallest set of atoms from the dictionary that allows for a perfect reconstruction of y. We also prove that under this same condition, there exists an iteration beyond which the algorithm converges exponentially.
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Recovery and Convergence Rate of the Frank–Wolfe Algorithm for the m-Exact-Sparse Problem
IEEE Transactions on Information Theory, 2019Co-Authors: Farah Cherfaoui, Valentin Emiya, Liva Ralaivola, Sandrine AnthoineAbstract:We study the properties of the Frank–Wolfe algorithm to solve the m-Exact-Sparse reconstruction problem, where a signal $y$ must be expressed as a Sparse Linear Combination of a predefined set of atoms, called dictionary . We prove that when the signal is Sparse enough with respect to the coherence of the dictionary, then the iterative process implemented by the Frank–Wolfe algorithm only recruits atoms from the support of the signal, is the smallest set of atoms from the dictionary that allows for a perfect reconstruction of $y$ . We also prove that under this same condition, there exists an iteration beyond which the algorithm converges exponentially.
Tianxiang Bai - One of the best experts on this subject based on the ideXlab platform.
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robust visual tracking using flexible structured Sparse representation
IEEE Transactions on Industrial Informatics, 2014Co-Authors: Tianxiang BaiAbstract:In this work, we propose a robust and flexible appearance model based on the structured Sparse representation framework. In our method, we model the complex nonLinear appearance manifold and the occlusion as a Sparse Linear Combination of structured union of subspaces in a basis library, which consists of multiple incremental learned target subspaces and a partitioned occlusion template set. In order to enhance the discriminative power of the model, a number of clustered background subspaces are also added into the basis library and updated during tracking. With the Block Orthogonal Matching Pursuit (BOMP) algorithm, we show that the new flexible structured Sparse representation based appearance model facilitates the tracking performance compared with the prototype structured Sparse representation model and other state of the art tracking algorithms.
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MFI - Flexible structured Sparse representation for robust visual tracking
2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2012Co-Authors: Tianxiang Bai, Yazhe TangAbstract:In this work, we propose a robust and flexible appearance model based on the structured Sparse representation framework. In our method, we model the complex nonLinear appearance manifold and occlusions as a Sparse Linear Combination of structured union of subspaces in a basis library consisting of multiple learned low dimensional subspaces and a partitioned occlusion template set. In order to enhance the discriminative power of the model, a number of clustered background subspaces are also added into the basis library and updated during tracking. With the Block Orthogonal Matching Pursuit (BOMP) algorithm, we show that the new structured Sparse representation based appearance model facilitates the tracking performance compared with the prototype model and other state of the art tracking algorithms.
Xuyong - One of the best experts on this subject based on the ideXlab platform.
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Supervised Sparse representation method with a heuristic strategy and face recognition experiments
Neurocomputing, 2012Co-Authors: Xuyong, Zuowangmeng, FanzizhuAbstract:In this paper we propose a supervised Sparse representation method for face recognition. We assume that the test sample could be approximately represented by a Sparse Linear Combination of all the ...
