The Experts below are selected from a list of 81399 Experts worldwide ranked by ideXlab platform

David Zhang - One of the best experts on this subject based on the ideXlab platform.

  • quadratic projection Based Feature Extraction with its application to biometric recognition
    Pattern Recognition, 2016
    Co-Authors: Hanzi Wang, Si Chen, David Zhang
    Abstract:

    This paper presents a novel quadratic projection Based Feature Extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the conventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, Based on the Lagrange duality theory, to extract nonlinear Features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recognition tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-Based Feature Extraction algorithm compared to the current state-of-the-art algorithms. HighlightsA novel quadratic projection Based Feature Extraction framework is developed.A set of quadratic matrices is learnt to distinguish each class from other classes.Quadratic matrix learning (QML) is formulated as an SDP problem.An efficient algorithm is developed to solve QML Based on Lagrange duality theory.Experiments on biometric recognition show the effectiveness of our algorithm.

  • quadratic projection Based Feature Extraction with its application to biometric recognition
    arXiv: Computer Vision and Pattern Recognition, 2016
    Co-Authors: Hanzi Wang, Si Chen, David Zhang
    Abstract:

    This paper presents a novel quadratic projection Based Feature Extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, Based on the Lagrange duality theory, to extract nonlinear Features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recogni- tion tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-Based Feature Extraction algorithm compared to the current state-of-the-art algorithms

  • accelerating the kernel method Based Feature Extraction procedure from the viewpoint of numerical approximation
    Neural Computing and Applications, 2011
    Co-Authors: Y. Xu, David Zhang
    Abstract:

    The kernel method suffers from the following problem: the computational efficiency of the Feature Extraction procedure is inversely proportional to the size of the training sample set. In this paper, from a novel viewpoint, we propose a very simple and mathematically tractable method to produce the computationally efficient kernel-method-Based Feature Extraction procedure. We first address the issue that how to make the Feature Extraction result of the reformulated kernel method well approximate that of the naive kernel method. We identify these training samples that statistically contribute much to the Feature Extraction results and exploit them to reformulate the kernel method to produce the computationally efficient kernel-method-Based Feature Extraction procedure. Indeed, the proposed method has the following basic idea: when one training sample has little effect on the Feature Extraction result and statistically has the high correlation with regard to all the training samples, the Feature Extraction term associated with this training sample can be removed from the Feature Extraction procedure. The proposed method has the following advantages: First, it proposes, for the first time, to improve the kernel method through formal and reasonable evaluation on the Feature Extraction term. Second, the proposed method improves the kernel method at a low extra cost and thus has a much more computationally efficient training phase than most of the previous improvements to the kernel method. The experimental comparison shows that the proposed method performs well in classification problems. This paper also intuitively shows the geometrical relation between the identified training samples and other training samples.

  • A THEORETICAL FRAMEWORK FOR MATRIX-Based Feature Extraction ALGORITHMS WITH ITS APPLICATION TO IMAGE RECOGNITION
    International Journal of Image and Graphics, 2008
    Co-Authors: Guiyu Feng, David Zhang, Jian Yang
    Abstract:

    Recently proposed matrix-Based methods, two-dimensional Principal Component Analysis (2DPCA), two-dimensional Linear Discriminant Analysis (2DLDA) and two-dimensional Locality Preserving Projections (2DLPP) have been shown to be effective ways to avoid the problems of high dimensionality and small sample sizes that are associated with vector-Based methods. In this paper, we propose a general theoretical framework for matrix-Based Feature Extraction algorithms from the point of view of graph embedding. Our framework can be applied to extend two recently proposed vector-Based algorithms, i.e. Unsupervised Discriminant Projection (UDP) and Marginal Fisher Analysis (MFA) algorithms, to their matrix-Based versions. Further, our framework can also be used as a platform to generate new matrix-Based Feature Extraction algorithms by designing meaningful graphs, e.g. two-dimensional Discriminant Embedding Analysis (2DDEA) in this paper. It is shown that 2DLDA is actually a special case of the 2DDEA method. Experiments on three publicly available image databases demonstrate the effectiveness of the proposed algorithm. Our results fit into the scene for a better picture about the matrix-Based Feature Extraction algorithms.

  • A THEORETICAL FRAMEWORK FOR MATRIX-Based Feature Extraction ALGORITHMS WITH ITS APPLICATION TO IMAGE RECOGNITION
    International Journal of Image and Graphics, 2008
    Co-Authors: Guiyu Feng, David Zhang, Jian Yang
    Abstract:

    Recently proposed matrix-Based methods, two-dimensional Principal Component Analysis (2DPCA), two-dimensional Linear Discriminant Analysis (2DLDA) and two-dimensional Locality Preserving Projections (2DLPP) have been shown to be effective ways to avoid the problems of high dimensionality and small sample sizes that are associated with vector-Based methods. In this paper, we propose a general theoretical framework for matrix-Based Feature Extraction algorithms from the point of view of graph embedding. Our framework can be applied to extend two recently proposed vector-Based algorithms, i.e. Unsupervised Discriminant Projection (UDP) and Marginal Fisher Analysis (MFA) algorithms, to their matrix-Based versions. Further, our framework can also be used as a platform to generate new matrix-Based Feature Extraction algorithms by designing meaningful graphs, e.g. two-dimensional Discriminant Embedding Analysis (2DDEA) in this paper. It is shown that 2DLDA is actually a special case of the 2DDEA method. Experime...

Majid Ahmadi - One of the best experts on this subject based on the ideXlab platform.

  • SMC - A Robust Wavelet Based Feature Extraction Method.
    2009
    Co-Authors: Iman Makaremi, Majid Ahmadi
    Abstract:

    In this paper, we propose a wavelet Based Feature Extraction method with a high tolerance to white Gaussian noise. This method is also computationally efficient. Along with an HMM classifier, this method is used for face recognition. High recognition rates in the presence of white Gaussian noises with different variances show this technique as a promising Feature Extraction method.

  • A robust wavelet Based Feature Extraction method for face recognition
    2009 IEEE International Conference on Systems Man and Cybernetics, 2009
    Co-Authors: Iman Makaremi, Majid Ahmadi
    Abstract:

    In this paper, we propose a wavelet Based Feature Extraction method with a high tolerance to white Gaussian noise. This method is also computationally efficient. Along with an HMM classifier, this method is used for face recognition. High recognition rates in the presence of white Gaussian noises with different variances show this technique as a promising Feature Extraction method.

  • ISNN (3) - An Efficient Wavelet Based Feature Extraction Method for Face Recognition
    Advances in Neural Networks – ISNN 2009, 2009
    Co-Authors: Iman Makaremi, Majid Ahmadi
    Abstract:

    A computationally efficient wavelet Based Feature Extraction method is proposed. This method is used for face recognition along with an HMM classifier. In comparison to similar method, this method needs less computation while the highest possible classification rate is still achievable. In this paper, different wavelet filters have been tried and effect of sub-image's size and overlap percentage in Feature Extraction on classification rate has been studied.

Tsuneo Nitta - One of the best experts on this subject based on the ideXlab platform.

  • Pitch-Synchronous Peak-Amplitude (PS-PA)-Based Feature Extraction Method for Noise-Robust ASR
    IEICE Transactions on Information and Systems, 2006
    Co-Authors: Muhammad Ghulam, Kouichi Katsurada, Junsei Horikawa, Tsuneo Nitta
    Abstract:

    A novel pitch-synchronous auditory-Based Feature Extraction method for robust automatic speech recognition (ASR) is proposed. A pitch-synchronous zero-crossing peak-amplitude (PS-ZCPA)-Based Feature Extraction method was proposed previously and it showed improved performances except when modulation enhancement was integrated with Wiener filter (WF)-Based noise reduction and auditory masking. However, since zero-crossing is not an auditory event, we propose a new pitch-synchronous peak-amplitude (PS-PA)-Based method to render the Feature extractor of ASR more auditory-like. We also examine the effects of WF-Based noise reduction, modulation enhancement, and auditory masking in the proposed PS-PA method using the Aurora-2J database. The experimental results show superiority of the proposed method over the PS-ZCPA and other conventional methods. Furthermore, the problem due to the reconstruction of zero-crossings from a modulated envelope is eliminated. The experimental results also show the superiority of PS over PA in terms of the robustness of ASR, though PS and PA lead to significant improvement when applied together.

  • ICASSP (1) - A Pitch-Synchronous Peak-Amplitude Based Feature Extraction Method for Noise Robust ASR
    2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 1
    Co-Authors: Muhammad Ghulam, Junsei Horikawa, Tsuneo Nitta
    Abstract:

    In this paper, we propose a novel pitch-synchronous auditory-Based Feature Extraction method for robust automatic speech recognition (ASR). A pitch-synchronous zero-crossing peak-amplitude (PS-ZCPA)-Based Feature Extraction method was proposed previously [1,2], and showed improved performance except while modulation enhancement was integrated together with Wiener filter (WF)-Based noise reduction and auditory masking into it [3]. However, since zero-crossing is not an auditory event, we propose a new pitch-synchronous peak-amplitude (PS-PA)-Based method to make a Feature extractor of ASR more auditory-like. We also examine the effect of WF-Based noise reduction, modulation enhancement, and auditory masking into the proposed PS-PA method using Aurora-2J database. The experimental results showed the superiority of the proposed method over the PS-ZCPA method, and eliminated the problem due to the reconstruction of zero-crossings from modulated envelope. The highest relative performance over MFCC was achieved as 67.33% using the PS-PA method together with WF-Based noise reduction, modulation enhancement, and auditory masking.

Hanzi Wang - One of the best experts on this subject based on the ideXlab platform.

  • quadratic projection Based Feature Extraction with its application to biometric recognition
    Pattern Recognition, 2016
    Co-Authors: Hanzi Wang, Si Chen, David Zhang
    Abstract:

    This paper presents a novel quadratic projection Based Feature Extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the conventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, Based on the Lagrange duality theory, to extract nonlinear Features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recognition tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-Based Feature Extraction algorithm compared to the current state-of-the-art algorithms. HighlightsA novel quadratic projection Based Feature Extraction framework is developed.A set of quadratic matrices is learnt to distinguish each class from other classes.Quadratic matrix learning (QML) is formulated as an SDP problem.An efficient algorithm is developed to solve QML Based on Lagrange duality theory.Experiments on biometric recognition show the effectiveness of our algorithm.

  • quadratic projection Based Feature Extraction with its application to biometric recognition
    arXiv: Computer Vision and Pattern Recognition, 2016
    Co-Authors: Hanzi Wang, Si Chen, David Zhang
    Abstract:

    This paper presents a novel quadratic projection Based Feature Extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, Based on the Lagrange duality theory, to extract nonlinear Features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recogni- tion tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-Based Feature Extraction algorithm compared to the current state-of-the-art algorithms

Iman Makaremi - One of the best experts on this subject based on the ideXlab platform.

  • SMC - A Robust Wavelet Based Feature Extraction Method.
    2009
    Co-Authors: Iman Makaremi, Majid Ahmadi
    Abstract:

    In this paper, we propose a wavelet Based Feature Extraction method with a high tolerance to white Gaussian noise. This method is also computationally efficient. Along with an HMM classifier, this method is used for face recognition. High recognition rates in the presence of white Gaussian noises with different variances show this technique as a promising Feature Extraction method.

  • A robust wavelet Based Feature Extraction method for face recognition
    2009 IEEE International Conference on Systems Man and Cybernetics, 2009
    Co-Authors: Iman Makaremi, Majid Ahmadi
    Abstract:

    In this paper, we propose a wavelet Based Feature Extraction method with a high tolerance to white Gaussian noise. This method is also computationally efficient. Along with an HMM classifier, this method is used for face recognition. High recognition rates in the presence of white Gaussian noises with different variances show this technique as a promising Feature Extraction method.

  • ISNN (3) - An Efficient Wavelet Based Feature Extraction Method for Face Recognition
    Advances in Neural Networks – ISNN 2009, 2009
    Co-Authors: Iman Makaremi, Majid Ahmadi
    Abstract:

    A computationally efficient wavelet Based Feature Extraction method is proposed. This method is used for face recognition along with an HMM classifier. In comparison to similar method, this method needs less computation while the highest possible classification rate is still achievable. In this paper, different wavelet filters have been tried and effect of sub-image's size and overlap percentage in Feature Extraction on classification rate has been studied.