The Experts below are selected from a list of 19125 Experts worldwide ranked by ideXlab platform
Licheng Jiao - One of the best experts on this subject based on the ideXlab platform.
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Double linear regressions for single labeled image per person face recognition
Pattern Recognition, 2014Co-Authors: Fei Yin, Licheng Jiao, Fanhua Shang, Lin Xiong, Shasha MaoAbstract:Recently the underlying sparse representation structure in high dimensional data has received considerable attention in pattern recognition and computer vision. In this paper, we propose a novel semi-supervised dimensionality reduction (SDR) method, named Double Linear Regressions (DLR), to tackle the Single Labeled Image per Person (SLIP) face recognition problem. DLR simultaneously seeks the best discriminating subspace and preserves the sparse representation structure. Specifically, a Subspace Assumption based Label Propagation (SALP) method, which is accomplished using Linear Regressions (LR), is first presented to propagate the label information to the unlabeled data. Then, based on the propagated labeled dataset, a sparse representation Regularization Term is constructed via Linear Regressions (LR). Finally, DLR takes into account both the discriminating efficiency and the sparse representation structure by using the learned sparse representation Regularization Term as a Regularization Term of Linear Discriminant Analysis (LDA). The extensive and encouraging experimental results on three publicly available face databases (CMU PIE, Extended Yale B and AR) demonstrate the effectiveness of the proposed method.
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Low-rank representation with local constraint for graph construction
Neurocomputing, 2013Co-Authors: Yaoguo Zheng, Xiangrong Zhang, Shuyuan Yang, Licheng JiaoAbstract:Graph-based semi-supervised learning has been widely researched in recent years. A novel Low-Rank Representation with Local Constraint (LRRLC) approach for graph construction is proposed in this paper. The LRRLC is derived from the original Low-Rank Representation (LRR) algorithm by incorporating the local information of data. Rank constraint has the capacity to capture the global structure of data. Therefore, LRRLC is able to capture both the global structure by LRR and the local structure by the locally constrained Regularization Term simultaneously. The Regularization Term is induced by the locality assumption that similar samples have large similarity coefficients. The measurement of similarity among all samples is obtained by LRR in this paper. Considering the non-negativity restriction of the coefficients in physical interpretation, the Regularization Term can be written as a weighted @?"1-norm. Then a semi-supervised learning framework based on local and global consistency is used for the classification task. Experimental results show that the LRRLC algorithm provides better representation of data structure and achieves higher classification accuracy in comparison with the state-of-the-art graphs on real face and digit databases.
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Training Hard-Margin Support Vector Machines Using Greedy Stagewise Algorithm
IEEE transactions on neural networks, 2008Co-Authors: Ling Wang, Licheng JiaoAbstract:Hard-margin support vector machines (HM-SVMs) suffer from getting overfitting in the presence of noise. Soft-margin SVMs deal with this problem by introducing a Regularization Term and obtain a state-of-the-art performance. However, this disposal leads to a relatively high computational cost. In this paper, an alternative method, greedy stagewise algorithm for SVMs, named GS-SVMs, is presented to cope with the overfitting of HM-SVMs without employing the Regularization Term. The most attractive property of GS-SVMs is that its computational complexity in the worst case only scales quadratically with the size of training samples. Experiments on the large data sets with up to 400 000 training samples demonstrate that GS-SVMs can be faster than LIBSVM 2.83 without sacrificing the accuracy. Finally, we employ statistical learning theory to analyze the empirical results, which shows that the success of GS-SVMs lies in that its early stopping rule can act as an implicit Regularization Term.
Xavier Maldague - One of the best experts on this subject based on the ideXlab platform.
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Total Variation Regularization Term-Based Low-Rank and Sparse Matrix Representation Model for Infrared Moving Target Tracking
Remote Sensing, 2018Co-Authors: Minjie Wan, Weixian Qian, Kan Ren, Qian Chen, Hai Zhang, Xavier MaldagueAbstract:Infrared moving target tracking plays a fundamental role in many burgeoning research areas of Smart City. Challenges in developing a suitable tracker for infrared images are particularly caused by pose variation, occlusion, and noise. In order to overcome these adverse interferences, a total variation Regularization Term-based low-rank and sparse matrix representation (TV-LRSMR) model is designed in order to exploit a robust infrared moving target tracker in this paper. First of all, the observation matrix that is derived from the infrared sequence is decomposed into a low-rank target matrix and a sparse occlusion matrix. For the purpose of preventing the noise pixel from being separated into the occlusion Term, a total variation Regularization Term is proposed to further constrain the occlusion matrix. Then an alternating algorithm combing principal component analysis and accelerated proximal gradient methods is employed to separately optimize the two matrices. For long-Term tracking, the presented algorithm is implemented using a Bayesien state inference under the particle filtering framework along with a dynamic model update mechanism. Both qualitative and quantitative experiments that were examined on real infrared video sequences verify that our algorithm outperforms other state-of-the-art methods in Terms of precision rate and success rate.
Ting-zhu Huang - One of the best experts on this subject based on the ideXlab platform.
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Bilateral filter based total variation Regularization for sparse hyperspectral image unmixing
Information Sciences, 2019Co-Authors: Jie Huang, Liang-jian Deng, Ting-zhu HuangAbstract:Abstract Spectral unmixing of hyperspectral images aims to find the proportion of constituent materials in mixed pixels. The total variation (TV) Regularization is widely included in classical sparse regression formulations to exploit the spatial information in hyperspectral data. It promotes piecewise constant transitions in the fractional abundance of the same endmember among neighboring pixels. The TV Regularization Term, however, usually brings some staircase effects. To alleviate this drawback, we propose a bilateral filter based TV Regularization for hyperspectral image unmixing. Then we present an unmixing model that combines a data-fidelity Term, a sparsity Regularization Term, and the new Regularization Term. To solve the proposed model, we design an algorithm called sparse unmixing via variable splitting augmented Lagrangian and bilateral filter based TV (SUnSAL-BF-TV), under the alternating direction method of multipliers (ADMM) framework. Our experimental results show that our algorithm is effective to unmix both simulated and real hyperspectral data sets.
Minjie Wan - One of the best experts on this subject based on the ideXlab platform.
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Total Variation Regularization Term-Based Low-Rank and Sparse Matrix Representation Model for Infrared Moving Target Tracking
Remote Sensing, 2018Co-Authors: Minjie Wan, Weixian Qian, Kan Ren, Qian Chen, Hai Zhang, Xavier MaldagueAbstract:Infrared moving target tracking plays a fundamental role in many burgeoning research areas of Smart City. Challenges in developing a suitable tracker for infrared images are particularly caused by pose variation, occlusion, and noise. In order to overcome these adverse interferences, a total variation Regularization Term-based low-rank and sparse matrix representation (TV-LRSMR) model is designed in order to exploit a robust infrared moving target tracker in this paper. First of all, the observation matrix that is derived from the infrared sequence is decomposed into a low-rank target matrix and a sparse occlusion matrix. For the purpose of preventing the noise pixel from being separated into the occlusion Term, a total variation Regularization Term is proposed to further constrain the occlusion matrix. Then an alternating algorithm combing principal component analysis and accelerated proximal gradient methods is employed to separately optimize the two matrices. For long-Term tracking, the presented algorithm is implemented using a Bayesien state inference under the particle filtering framework along with a dynamic model update mechanism. Both qualitative and quantitative experiments that were examined on real infrared video sequences verify that our algorithm outperforms other state-of-the-art methods in Terms of precision rate and success rate.
Rafael Verdú-monedero - One of the best experts on this subject based on the ideXlab platform.
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Fractional Regularization Term for Variational Image Registration
Mathematical Problems in Engineering, 2009Co-Authors: Rafael Verdú-monedero, Jorge Larrey-ruiz, Juan Morales-sánchez, José-luis Sancho-gómezAbstract:Image registration is a widely used task of image analysis with applications in many fields. Its classical formulation and current improvements are given in the spatial domain. In this paper a Regularization Term based on fractional order derivatives is formulated. This Term is defined and implemented in the frequency domain by translating the energy functional into the frequency domain and obtaining the Euler-Lagrange equations which minimize it. The new Regularization Term leads to a simple formulation and design, being applicable to higher dimensions by using the corresponding multidimensional Fourier transform. The proposed Regularization Term allows for a real gradual transition from a diffusion registration to a curvature registration which is best suited to some applications and it is not possible in the spatial domain. Results with 3D actual images show the validity of this approach.
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Fast communication: Generalized Regularization Term for non-parametric multimodal image registration
Signal Processing, 2007Co-Authors: Jorge Larrey-ruiz, Juan Morales-sánchez, Rafael Verdú-monederoAbstract:In the field of non-rigid medical image registration, many regularizers based on first- or second-order derivatives have been studied. In this paper, a new Regularization Term based on fractional order derivatives is proposed for the registration of multimodal (e.g., medical) images. It can be seen as a generalization of the diffusion and curvature smoothing Terms, but with this approach it is possible to obtain better registration results in Terms of both similarity of the images and smoothness of the transformation. This registration scheme is tested on two realistic medical imaging scenarios, comparing the obtained results with the optimally registered diffusion and curvature cases.