近几年的论文和代码_effective texture classification by texton encodin

       [1]       J. Xu, L. Zhang, W. Zuo, D. Zhang, and X. Feng, “Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising,” in ICCV 2015. (paper, sup) (code) (From “patch” based learning to “patch group” based learning!)

       [2]       S. Gu, W. Zuo, Q. Xie, D. Meng, X. Feng, L. Zhang, “Convolutional Sparse Coding for Image Super-resolution,” in ICCV 2015. (paper, sup) (code) (State-of-the-art super-resolution result!)

       [3]       F. Chen, L. Zhang, and H. Yu, “External Patch Prior Guided Internal Clustering for Image Denoising,” ICCV 2015. (paper,sup) (code) (Exploit external and internal information jointly for high performance denoising!)

       [4]       Lin Zhang, Lei Zhang, and Alan C. Bovik, “A Feature-Enriched Completely Blind Image Quality Evaluator,” IEEE Trans. on Image Processing, vol. 24, issue 8, pp. 2579 – 2591, Aug. 2015. (paper) (code and website) (An "opinion-unaware" method which outperforms all "opinion-aware" methods!)

       [5]       W. Zuo, D. Ren, S. Gu, L. Lin, and L. Zhang, “Discriminative Learning of Iteration-wise Priors for Blind Deconvolution,” in CVPR 2015. (paper, sup) (A highly effective blind image deblurring algorithm!)  

       [6]       F. Wang, W. Zuo, L. Zhang, Deyu Meng, and David Zhang, “A Kernel Classification Framework for Metric Learning,” to appear in IEEE Transactions on Neural Networks and Learning Systems. (paper) (code) (We make metric learning hundred to thousand times faster!)

       [7]       S. Gu, L. Zhang, W. Zuo, and X. Feng, “Projective Dictionary Pair Learning for Pattern Classification,” In NIPS 2014.(paper, sup)  (code) (From “dictionary learning” to “dictionary pair learning”!)

       [8]       P. Zhu, M. Yang, L. Zhang, and Il-Yong Lee, “Local Generic Representation for Face Recognition with Single Sample per Person,” In ACCV 2014. (paper) (code)

       [9]       K. Zhang, L. Zhang, Q. Liu, D. Zhang, and M-H. Yang, “Fast Tracking via Dense Spatio-Temporal Context Learning,” InECCV 2014. (paper) (code and website)

    [10]    S. Cai, W. Zuo, L. Zhang, X. Feng, and P. Wang, “Support Vector Guided Dictionary Learning,” In ECCV 2014. (paper, sup) (code)

    [11]    Q. Wang, W. Zuo, L. Zhang, and P. Li, “Shrinkage Expansion Adaptive Metric Learning,” In ECCV 2014. (paper, sup) (code)

    [12]    S. Gu, L. Zhang, W. Zuo, and X. Feng, “Weighted Nuclear Norm Minimization with Application to Image Denoising,” In CVPR 2014. (paper) (sup) (code) (Excellent denoising results in terms of both PSNR and visual quality!)

Q. Xie, D. Meng, S. Gu, L. Zhang, W. Zuo, X. Feng and Z. Xu, “On the Optimal Solution of Weighted Nuclear Norm Minimization” Technical Report, arXiv: 1405.6012. (report) (In this technical report, we give a more complete analysis of the optimal solution of WNNM.)

    [13]    W. Lian and L. Zhang, “Point Matching in the Presence of Outliers in Both Point Sets: A Concave Optimization Approach,” In CVPR 2014. (paper) (sup) (code will be available soon)

    [14]    Q. Zhao, D. Meng, Z. Xu, W. Zuo, and L. Zhang, “Robust principal component analysis with complex noise,” In ICML 2014. (paper) (code)

    [15]    W. Xue, X. Mou, L. Zhang, and X. Feng, “Perceptual Fidelity Aware Mean Squared Error,” In ICCV 2013. (paper) (code) (We proved, both empirically and theoretically, that the MSE of the smoothed images can work very well for FR-IQA!)

    [16]    W. Zuo, D. Meng, L. Zhang, X. Feng, and D. Zhang, “A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding,” In ICCV 2013. (paper, sup) (code) (The corrected solution of non-convex sparse coding by iterated thresholding!)

    [17]    P. Zhu, L. Zhang, W. Zuo, and D. Zhang, “From Point to Set: Extend the Learning of Distance Metrics,” In ICCV 2013. (paper) (code)(We extended the metric learning from point-to-point to point-to-set and set-to-set!)

    [18]    P. Li, Q. Wang, and L. Zhang, “A Novel Earth Mover's Distance Methodology for Image Matching with Gaussian Mixture Models,” In ICCV 2013. (paper) (code) (A new framework for image classification.)

    [19]    P. Li, Q. Wang, W. Zuo, and L. Zhang, “Log-Euclidean Kernels for Sparse Representation and Dictionary Learning,” In ICCV 2013. (paper) (code) (Sparse representation and dictionary learning in a new space.)

    [20]    M. Yang, Luc Van Gool, and L. Zhang, “Sparse Variation Dictionary Learning for Face Recognition with A Single Training Sample Per Person,” In ICCV 2013. (paper) (code) (Dictionary learning with a generic dataset for face recognition with a single training sample.)

    [21]    W. Zuo, L. Zhang, C. Song, and D. Zhang, “Texture Enhanced Image Denoising via Gradient Histogram Preservation,” In CVPR 2013.(paper) (code)

    [22]    W. Xue, L. Zhang, and X. Mou, “Learning without Human Scores for Blind Image Quality Assessment,” In CVPR 2013. (paper) (code)

    [23]    D. Meng, Z. Xu, L. Zhang, and J. Zhao, “A Cyclic Weighted Median Method for L1 Low-Rank Matrix Factorization with Missing Entries,” In AAAI 2013. (paper) (code) (A very simple but very efficient and effective L1 matrix factorization algorithm.)

    [24]    S. Wang, L. Zhang, and Y. Liang, “Nonlocal Spectral Prior Model for Low-level Vision,” In ACCV12. (paper) (code will be available soon)

    [25]    K. Zhang, L. Zhang, and M.H. Yang, “Real-time Compressive Tracking,” In ECCV 2012. (paper) (code and website) (No training, no feature selection, speed up-to 40fps under Matlab, but with state-of-the-art tracking performance in terms of both success rate and centerlocation error!)

    [26]    B. Peng and L. Zhang, “Evaluation of Image Segmentation Quality by Adaptive Ground Truth Composition,” In ECCV 2012. (paper) (code and website) (A novel metric to evaluate the quality of image segmentation!)

    [27]    W. Lian and L. Zhang, “Robust Point Matching Revisited: A Concave Optimization Approach,” In ECCV 2012. (paper) (code)

    [28]    M. Yang, L. Zhang, and D. Zhang, “Efficient Misalignment-Robust Representation for Real-Time Face Recognition,” In ECCV 2012. (paper) (code)

    [29]    P. Zhu, L. Zhang, Q. Hu, and Simon C.K. Shiu, “Multi-scale Patch based Collaborative Representation for Face Recognition with Margin Distribution Optimization,” In ECCV 2012. (paper) (code)

    [30]    M. Yang, L. Zhang, D. Zhang, and S. Wang, “Relaxed Collaborative Representation for Pattern Classification,” In CVPR 2012. (paper) (code)

    [31]    S. Wang, L. Zhang, Y. Liang, and Q. Pan, “Semi-Coupled Dictionary Learning with Applications to Image Super-Resolution and Photo-Sketch Image Synthesis,” In CVPR 2012. (paper) (code and website)

    [32]    L. Zhang, M. Yang, and X. Feng, “Sparse Representation or Collaborative Representation: Which Helps Face Recognition?” In ICCV 2011. (paper, code)

    [33]    M. Yang, L. Zhang, X. Feng, and D. Zhang, “Fisher Discrimination Dictionary Learning for Sparse Representation,” In ICCV 2011. (paper, code)

    [34]    L. Zhang, P. Zhu, Q. Hu, and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” In ICCV 2011. (paper, code)

    [35]    W. Dong, L. Zhang, and G. Shi, “Centralized Sparse Representation for Image Restoration,” In ICCV 2011. (paper, code)

    [36]    Meng Yang, Lei Zhang, Jian Yang, and David Zhang, “Robust Sparse Coding for Face Recognition,” In CVPR 2011. (paper) (code)

    [37]    Weisheng Dong, Xin Li, Lei Zhang, and Guangming Shi, “Sparsity-based Image Denoising via Dictionary Learning and Structural Clustering,” In CVPR 2011 (oral). (paper) (code)

    [38]    M. Yang and L. Zhang, “Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary,” In ECCV 2010. (code)

    [39]    W. Lian and L. Zhang, “Rotation invariant non-rigid shape matching in cluttered scenes,” In ECCV 2010. (code)

    [40]    W. Li, L. Zhang, D. Zhang, G. Lu, and J. Yan, “Efficient Joint 2D and 3D Palmprint Matching with Alignment Refinement,” In CVPR 2010.(database)

    [41]    Q. Zhao, L. Zhang, D. Zhang, W. Huang, and J. Bai, “Curvature and Singularity Driven Diffusion for Oriented Pattern Enhancement with Singular Points,” In CVPR09.

    [42]    L. Zhang, Q. Gao, and D. Zhang, “Directional Independent Component Analysis with Tensor Representation,” In CVPR2008, pp.1-7, 23-28, June, Anchorage, Alaska, U.S. (Oral).

    [43]    Lin Zhang, Lei Zhang, X. Mou, and D. Zhang, “A Comprehensive Evaluation of Full Reference Image Quality Assessment Algorithms,” InICIP 2012. (paper)

    [44]    W. Dong, G. Shi, L. Zhang, and X. Wu, “Super-resolution with nonlocal regularized sparse representation,” In SPIE VCIP 2010. (Best paper award)

    [45]    Meng Yang, Lei Zhang, Daivd Zhang, and Jian Yang, “Metaface Learning for Sparse Representation based Face Recognition,” In ICIP 2010. (code)

    [46]    Lin Zhang, Lei Zhang, Zhenhua Guo, and David Zhang, “MONOGENIC-LBP: A NEW APPROACH FOR ROTATION INVARIANT TEXTURE CLASSIFICATION,” In ICIP 2010. (code)

    [47]    Lin Zhang, Lei Zhang, and X. Mou, “RFSIM: A Feature based Image Quality Assessment Metric using Riesz Transforms,” In ICIP 2010.(code)

    [48]    Kaihua Zhang, Lei Zhang, and Su Zhang, “A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction,” In ICIP 2010. (code)

    [49]    Qijun Zhao, Feng Liu, Lei Zhang, and David Zhang, “A comparative study on quality assessment of high resolution fingerprint images,” In ICIP 2010.

    [50]    Jin Xie, Lei Zhang, Jane You, and David Zhang, “TEXTURE CLASSIFICATION VIA PATCH-BASED SPARSE TEXTON LEARNING,” In ICIP 2010.

    [51]    Meng Yang, Lei Zhang, Lin Zhang, and David Zhang, “Monogenic Binary Pattern (MBP): A Novel Feature Extraction and Representation Model for Face Recognition,” In ICPR 2010.

    [52]    Lei Zhang, Meng Yang, Zhizhao Feng, and David Zhang, “On the Dimensionality Reduction for Sparse Representation based Face Recognition,” In ICPR 2010. (paper) (code)

    [53]    Qijun Zhao, Feng Liu, Lei Zhang, and David Zhang, “Parallel versus Hierarchical Fusion of Extended Fingerprint Features,” In ICPR 2010.

    [54]    Feng Liu, Qijun Zhao, Lei Zhang, and David Zhang, “Fingerprint Pore Matching based on Sparse Representation,” In ICPR 2010.

    [55]    Bob Zhang, Lei Zhang, Jane You, and Fakhri Karray, “Microaneurysm (MA) Detection via Sparse Representation Classifier with MA and Non-MA Dictionary Learning,” In ICPR 2010. (paper)

    [56]    B. Peng, L. Zhang, and J. Yang, “Iterated Graph Cuts for Image Segmentation,” In ACCV 2009. (software)

    [57]    Lin Zhang, Lei Zhang, and D. Zhang, “A Multi-Scale Bilateral Structure Tensor Based Corner Detector,” In ACCV 2009. (code)

    [58]    Weisheng Dong, Lei Zhang, Guangming Shi, and Xiaolin Wu, “Nonlocal back-projection for adaptive image enlargement,” In ICIP 2009.(code)

    [59]    Lin Zhang, Lei Zhang, and David Zhang, “Finger-Knuckle-Print: A New Biometric Identifier,” In ICIP 2009.

    [60]    Lin Zhang, Lei Zhang, and David Zhang, “Finger-Knuckle-Print Verification Based On Band-Limited Phase-Only Correlation,” The 13th International Conference on Computer Analysis of Images and Patterns (CAIP09).

    [61]    Q. Zhao, L. Zhang, D. Zhang, and N. Luo, “Direct Pore Matching for Fingerprint Recognition,” International Conference on Biometrics 2009 (ICB09), pp. 597-606, Alghero, Italy, June 2-5, 2009.

    [62]    Wei Li, Lei Zhang, and David Zhang, “Three Dimensional Palmprint Recognition,” 2009 IEEE International Conference on Systems, Man, and Cybernetics, SMC09.

    [63]    X. Li, B. Gunturk, and L. Zhang, “Image demosaicing: a systematic survey,” Visual Communications and Image Processing 2008, Proceedings of the SPIE, Volume 6822, pp. 68221J-68221J-15 (2008). San Jose, CA, USA

    [64]    Q. Zhao, L. Zhang, D. Zhang, and N. Luo, “Adaptive Pore Model for Fingerprint Pore Extraction,” Proceedings of International Conference on Pattern Recognition 2008 (ICPR08), pp. 1-4, Tampa, Florida, USA, Dec. 8-11, 2008.

    [65]    D. Zhang, G. Lu, W. Li, L. Zhang, and N. Luo, “Three Dimensional Palmprint Recognition using Structured Light Imaging,” 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS08), Sept. 29-Oct. 1 2008, pp. 1-6. Hyatt Regency Crystal City, U.S.

    [66]    Lei Zhang, Zhenhua Guo, Zhou Wang, and David Zhang, “Palmprint verification using complex wavelet transform,” In ICIP07, September 16-19, 2007, San Antonio, Texas, USA. Volume 2, Page(s): II - 417 - II – 420.

    [67]    Marko Slyz and Lei Zhang, “A Block-based Inter-band Lossless Hyperspectral Image Compressor,” DCC05 (Data Compression Conference) 2005, pp.427-436, Cliff Lodge, USA, 29-31 March 2005.

    [68]    W. Zuo, L. Zhang, C. Song, D. Zhang, and H. Gao, “Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising,” IEEE Trans. on Image Processing, vol. 23, issue 6, pp. 2459-2472, June 2014. (paper) (code) (This is an extension of our GHP work in CVPR’13)

    [69]    J. Jiang, L. Zhang, and J. Yang, “Mixed Noise Removal by Weighted Encoding with Sparse Nonlocal Regularization,” IEEE Trans. on Image Processing, vol. 23, issue 6, pp. 2651-2662, June 2014. (paper) (sup) (code)

    [70]    W. Dong, L. Zhang, G. Shi, and X. Li, “Nonlocally Centralized Sparse Representation for Image Restoration,” IEEE Trans. on Image Processing, vol. 22, no. 4, pp. 1620-1630, Apr. 2013. (paper) (website) (code) (This paper is an improvement of our ICCV11 paper “Centralized Sparse Representation for Image Restoration”.)

    [71]    W. Dong, L. Zhang, R. Lukac, and G. Shi, “Sparse Representation based Image Interpolation with Nonlocal Autoregressive Modeling,”IEEE Trans. on Image Processing, vol. 22, no. 4, pp. 1382-1394, Apr. 2013. (paper) (website) (code)

    [72]    W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. on Image Processing, vol. 20, no. 7, pp. 1838-1857, July 2011. (paper, matlab  code & website)

    [73]    L. Zhang, W. Dong, D. Zhang, and G. Shi, “Two-stage Image Denoising by Principal Component Analysis with Local Pixel Grouping,”Pattern Recognition, vol. 43, issue 4, pp. 1531-1549, April 2010. (paper, matlab code, website) (Code optimized!)

    [74]    L. Zhang and X. Wu, “An edge-guided image interpolation algorithm via directional filtering and data fusion,” IEEE Trans. on Image Processing, vol. 15, pp. 2226-2238, Aug. 2006. (paper, matlab code)

    [75]    L. Zhang, X. Wu, A. Buades, and X. Li, “Color Demosaicking by Local Directional Interpolation and Non-local Adaptive Thresholding,”Journal of Electronic Imaging 20(2), 023016 (Apr-Jun 2011), DOI:10.1117/1.3600632. (paper, website and dataset, code)

    [76]    L. Zhang, W. Dong, X. Wu, and G. Shi, “Spatial-Temporal Color Video Reconstruction from Noisy CFA Sequence,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 20, no. 6, pp. 838-847, June 2010. (paper)

    [77]    L. Zhang, R. Lukac, X. Wu, and D. Zhang, “PCA-based Spatially Adaptive Denoising of CFA Images for Single-Sensor Digital Cameras,”IEEE Trans. on Image Processing, vol. 18, no. 4, pp. 797-812, April 2009. (paper, matlab code, website)

    [78]    L. Zhang, X. Wu, and D. Zhang, “Color Reproduction from Noisy CFA Data of Single Sensor Digital Cameras,” IEEE Trans. Image Processing, vol. 16, no. 9, pp. 2184-2197, Sept. 2007.  (paper, matlab code, website)

    [79]    L. Zhang, X. Li, and D. Zhang, “Image Denoising and Zooming under the LMMSE Framework,” IET Image Processing, Vol. 6, Issue 3, pp. 273–283, 2012. (paper) (code)

    [80]    L. Zhang and X. Wu, “Color demosaicking via directional linear minimum mean square-error estimation,” IEEE Trans. on Image Processing, vol. 14, pp. 2167-2178, Dec. 2005. (paper, matlab code)

    [81]    F. Zhang, X. Wu, X. Yang, W. Zhang, and L. Zhang, “Robust Color Demosaicking with Adaptation to Varying Spectral Correlations,”IEEE Trans. on Image Processing, vol. 18, no. 12, pp. 2706-2717, Dec 2009. (paper)

    [82]    X. Wu and L. Zhang, “Improvement of color video demosaicking in temporal domain,” IEEE Trans. on Image Processing, vol. 15, pp. 3138-3151, Oct. 2006. (paper, software)

    [83]    X. Wu and L. Zhang, “Temporal color video demosaicking via motion estimation and data fusion,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 16, pp. 231-240, Feb. 2006. (paper)

    [84]    L. Zhang, B. Paul, and X. Wu, “Multiscale LMMSE-based image denoising with optimal wavelet selection,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 15, pp. 469-481, April 2005. (paper, matlab code)

    [85]    Q. Pan, L. Zhang, H. Zhang, and G. Dai, “Two de-noising methods by wavelet transform,” IEEE Trans. on Signal Processing, vol. 47, pp. 3401-3406, Dec. 1999. (paper)

    [86]    W. Xue, X. Mou, L. Zhang, A. Bovik, and X. Feng, “Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features,” IEEE Trans. on Image Processing, vol. 23, issue 11, pp. 4850 – 4862, Nov. 2014. (paper) (code)

    [87]    W. Xue, L. Zhang, X. Mou, and A. C. Bovik, “Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index,” IEEE Transactions on Image Processing, vol. 23, issue 2, pp. 684 - 695, Feb., 2014. (paper) (code) (website) (A very simple but highly efficient and effective full reference IQA algorithm!)

    [88]    Lin Zhang, Lei Zhang, X. Mou, and D. Zhang, “FSIM: A Feature Similarity Index for Image Quality Assessment,” IEEE Trans. Image Processing, vol. 20, no. 8, pp. 2378-2386, 2011. (paper, website & code)

    [89]    M. Zhang, X. Mou, and L. Zhang, “Non-Shift Edge based Ratio (NSER): An Image Quality Assessment Metric Based on Early Vision Features,” IEEE Signal Processing Letters, vol. 18, no. 5, pp. 315-318, May, 2011. (paper)

    [90]    M. Yang, L. Zhang, X. Feng, and D. Zhang, “Sparse Representation based Fisher Discrimination Dictionary Learning for Image Classification,” International Journal of Computer Vision, vol. 109, issue 3, pp. 209-232, Sept. 2014. (paper) (code) (This is an extension of our FDDL work in ICCV’11)

    [91]    P. Zhu, W. Zuo, L. Zhang, S. Shiu, and D. Zhang, “Image Set based Collaborative Representation for Face Recognition,” IEEE Trans. on Information Forensics and Security, vol. 9, no. 7, pp. 1120-1132, July 2014. (paper) (code)

    [92]    L. Zhang, M. Yang, X. Feng, Y. Ma, and D. Zhang, “Collaborative Representation based Classification for Face Recognition,” Technical report. arXiv: 1204.2358. (paper) (code) (This is a substantial extension of our ICCV11 paper “Sparse Representation or Collaborative Representation: Which Helps Face Recognition?”)

    [93]    M. Yang, L. Zhang, J. Yang, and D. Zhang, “Regularized Robust Coding for Face Recognition,” IEEE Transactions on Image Processing, Volume 22, Issue 5, Pages 1753-1766, May 2013. (paper) (code) (This paper is a substantial extension of our CVPR11 paper “Robust sparse coding for face recognition”.)

    [94]    M. Yang, L. Zhang, S. Shiu, and D. Zhang, “Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition,” IEEE Trans. on Information Forensics and Security, vol. 7, no. 6, pp. 1738-1751, Dec. 2012. (paper) (code) (In this work we proposed a new binary coding scheme, namely MBC, which has very high efficiency and accuracy in face representation and recognition.)

    [95]    M. Yang, L. Zhang, S. Shiu, and D. Zhang, “Robust Kernel Representation with Statistical Local Features for Face Recognition,” IEEE Transactions on Neural Networks and Learning Systems, Volume 24, Issue 6, Pages 900-912, June 2013. (paper) (code)

    [96]    M. Yang, Z. Feng, S. Shiu, and L. Zhang, “Fast and Robust Face Recognition via Coding Residual Map Learning based Adaptive Masking,” Pattern Recognition, vol. 47, no. 2, pp. 535-543, Feb. 2014. (paper) (code will be available soon)

    [97]    Z. Feng*, M. Yang*, L. Zhang, Y. Liu, and D. Zhang, “Joint Discriminative Dimensionality Reduction and Dictionary Learning for Face Recognition,” Pattern Recognition, Volume 46, Issue 8, Pages 2134-2143, Aug. 2013. (*The two authors contribute equally.) (paper) (code)

    [98]    M. Yang, L. Zhang, S. Shiu, and D. Zhang, “Gabor Feature based Robust Representation and Classification for Face Recognition with Gabor Occlusion Dictionary,” Pattern Recognition, Volume 46, Issue 7, Pages 1865-1878, July 2013. (paper) (code)

    [99]    J. Yang, D. Chu, L. Zhang, Y. Xu, and J. Yang, “Sparse Representation Classifier Steered Discriminative Projection with Applications to Face Recognition,” IEEE Transactions on Neural Networks and Learning Systems, Volume 24, Issue 7, Pages 1023-1035, July 2013.

  [100]  J. Yang, L. Zhang, Y. Xu, and Jing-yu Yang, “Beyond Sparsity: the Role of L1-optimizer in Pattern Classification,” Pattern Recognition, vol. 45, issue 3, Pages 1104–1118, March 2012.

  [101]  J. Yang, L. Zhang, J. Yang, and D. Zhang, “From Classifiers to Discriminators: A Nearest Neighbor Rule Induced Discriminant Analysis,”Pattern Recognition, vol. 44, issue 7, pp. 1387-1402, July 2011.

  [102]  W. Yang, C.Y. Sun, and L. Zhang, “A Multi-Manifold Discriminant Analysis Method for Image Feature Extraction,” Pattern Recognition,vol. 44, issue 8, pp. 1649-1657, August 2011. (paper)

  [103]  B. Zhang, L. Zhang, D. Zhang, and L. Shen, “Directional Binary Code with Application to PolyU Near-Infrared Face Database,” Pattern Recognition Letters, vol. 31, issue 14, pp. 2337-2344, Oct. 2010. (paper) (database)

  [104]  W. Di, L. Zhang, D. Zhang, and Q. Pan, “Studies on Hyperspectral Face Recognition in Visible Spectrum with Feature Band Selection,”IEEE Trans. on System, Man and Cybernetics, Part A, vol. 40, issue 6, pp. 1354 – 1361, Nov. 2010. (paper) (database)

  [105]  J. Yang, C. Liu, and L. Zhang, “Color Space Normalization: Enhancing the Discriminating Power of Color Spaces for Face Recognition,”Pattern Recognition, 2010, 43(4), 1454-1466, April 2010. (paper)

  [106]  Q. Gao, L. Zhang, D. Zhang, and H. Xu, “Independent components extraction from image matrix,” Pattern Recognition Letters, vol. 31, issue 3, pp. 171 – 178, Feb. 2010. (paper)

  [107]  Q. Gao, L. Zhang, and D. Zhang, “Sequential Row-Column Independent Component Analysis for Face Recognition,” Neurocomputing,vol. 72, pp. 1152–1159, Jan. 2009. (paper)

  [108]  Q. Gao, L. Zhang, and D. Zhang, “Face Recognition using FLDA with Single Training Image Per-person,” Applied Mathematics and Computation, vol. 205, pp. 726-734, 2008. (paper, code)

  [109]  Y. Zhao, L. Zhang, and S. Kong, “Band Subset Based Clustering and Fusion for Hyperspectral Imagery Classification,” IEEE Trans. onGeoscience and Remote Sensing, vol. 49, no. 2, pp. 747-756, Feb. 2011. (paper)

  [110]  B. Peng, L. Zhang, and D. Zhang, “A Survey of Graph Theoretical Approaches to Image Segmentation,” Pattern Recognition, Volume 46, Issue 3, Pages 1020-1038, Mar. 2013. (paper)

  [111]  K. Zhang, L. Zhang, H. Song, and D. Zhang, “Re-initialization Free Level Set Evolution via Reaction Diffusion,” IEEE Transactions on Image Processing, Volume 22, Issue 1, Pages 258-271, Jan. 2013. (paper) (code and website) (This work unifies the level set evolution under the reaction diffusion framework, which is completely free of re-initialization.)

  [112]  S. Li, H. Lu, and L. Zhang, “Arbitrary body segmentation in static images,” Pattern Recognition, Volume 45, Issue 9, Pages 3402–3413, Sept. 2012.

  [113]  B. Peng, L. Zhang, and D. Zhang, “Automatic Image Segmentation by Dynamic Region Merging,” IEEE Trans. on Image Processing, vol. 12, no. 12, pp. 3592-3605, 2011. (paper, software, website) (Source code)

  [114]  B. Peng, L. Zhang, D. Zhang, and J. Yang, “Image Segmentation by Iterated Region Merging with Localized Graph Cuts,” Pattern Recognition, vol. 44, issues 10-11, pp. 2527-2538, October-November 2011. (paper) (software)

  [115]  K. Zhang, L. Zhang, H. Song, and W. Zhou, “Active contours with selective local or global segmentation: a new formulation and level set method,” Image and Vision Computing, vol. 28, issue 4, pp. 668-676, April 2010. (paper, matlab code, website)

  [116]  K. Zhang, H. Song, and L. Zhang, “Active Contours Driven by Local Image Fitting Energy,” Pattern recognition, vol. 43, issue 4, pp. 1199-1206, April 2010. (paper, matlab code)

  [117]  J. Ning, L. Zhang, D. Zhang, and C. Wu, “Interactive Image Segmentation by Maximal Similarity based Region Merging,” Pattern Recognition, vol. 43, pp. 445-456, Feb, 2010. (paper, website & code)

  [118]  Y. Zhao, L. Zhang, D. Zhang, and Q. Pan, “Object Separation by Polarimetric and Spectral Imagery Fusion,” Computer Vision and Image Understanding, vol. 113, no. 8, pp. 855-866, Aug. 2009.  (paper, dataset)

  [119]  K. Zhang, L. Zhang, and M. Yang, “Fast Compressive Tracking,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 36, no. 10, pp. 2002-2015, Oct. 2014. (paper) (code and website) (This is an extension of our CT tracker in ECCV’12)

  [120]  K. Zhang, L. Zhang, and M. Yang, “Real-time Object Tracking via Online Discriminative Feature Selection,” IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 4664-4677, Dec. 2013. (paper) (code)

  [121]  K. Zhang, L. Zhang, M. Yang, and Q. Hu, “Robust Object Tracking via Active Feature Selection,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 11, pp. 1957-1967, Nov. 2013. (paper) (code)

  [122]  J. Ning, L. Zhang, D. Zhang, and W. Yu, “Joint Registration and Active Contour Segmentation for Object Tracking,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 9, pp. 1589 -1597, Sept. 2013. (paper) (code) (website)

  [123]  J. Ning, L. Zhang, D. Zhang, and C. Wu, “Scale and Orientation Adaptive Mean Shift Tracking,” IET Computer Vision, vol. 6, no.1, pp. 62-69, 2012. (paper, website. code)

  [124]  J. Ning, L. Zhang, D. Zhang, and C. Wu, “Robust Mean Shift Tracking with Corrected Background-Weighted Histogram,” IET Computer Vision, vol. 6, no.1, pp. 52-61, 2012. (paper, website. code) (We prove that the background-weighted histogram in the original mean-shift tracking method is incorrect.)

  [125]  J. Ning, L. Zhang, D. Zhang, and C. Wu, “Robust Object Tracking using Joint Color-Texture Histogram,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 23, No. 7 (2009) 1245–1263. (paper, code)

  [126]  J. Xie, L. Zhang, J. You and S. Shiu, “Effective Texture Classification by Texton Encoding Induced Statistical Features,” Pattern Recognition, vol. 48, issue 2, pp. 447-457, February 2015. (paper) (code) (A very effective texture classification scheme! The code has been updated by including the texton learning part.)

  [127]  Z. Guo, L. Zhang, and D. Zhang, “A Completed Modeling of Local Binary Pattern Operator for Texture Classification,” IEEE Trans. on Image Processing, vol. 19, no. 6, pp. 1657-1663, June 2010. (paper, matlab code)

  [128]  Z. Guo, L. Zhang, and D. Zhang, “Rotation Invariant Texture Classification using LBP Variance (LBPV) with Global Matching,” Pattern Recognition, vol. 43, no. 3, pp. 706-719, Mar. 2010.  (paper, matlab code)

  [129]  G. Gao, L. Zhang, J. Yang, L. Zhang, and D. Zhang, “Reconstruction based Finger-Knuckle-Print Verification with Score Level Adaptive Binary Fusion,” IEEE Transactions on Image Processing, vol. 22, issue 12, pp. 5050-5062, Dec., 2013. (paper)

  [130]  Lin Zhang, Lei Zhang, D. Zhang, and Z. Guo, “Phase Congruency Induced Local Features for Finger-Knuckle-Print Recognition,” Pattern Recognition, Volume 45, Issue 7, Pages 2522-2531, July 2012. (paper) (website)

  [131]  Lin Zhang, Lei Zhang, D. Zhang, and H. Zhu, “Ensemble of Local and Global Information for Finger-Knuckle-Print Recognition,” Pattern Recognition, vol. 44, no. 9, pp. 1990-1998, Sep. 2011. (paper) (website)

  [132]  Lin Zhang, Lei Zhang, D. Zhang, and H. Zhu, “Online Finger-Knuckle-Print Verification for Personal Authentication,” Pattern Recognition, vol. 43, no. 7, pp. 2560-2571, July 2010. (paper) (website)

  [133]  Z. Guo, D. Zhang, L. Zhang, and W. Liu, “Feature Band Selection for Online Multispectral Palmprint Recognition,” IEEE Trans. on Information Forensics and Security., vol. 7, issue 3, pp. 1094-1099, Mar. 2012.

  [134]  J. Xie, L. Zhang, J. You, D. Zhang, and X. Qu, “A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification,” Sensors, Volume 12, Issue 7, Pages 8691-8709, June 2012. (paper)

  [135]  W. Li, B. Zhang, L. Zhang, and J. Yan, “Principal Line-Based Alignment Refinement for Palmprint Recognition,” IEEE Transactions on System, Man and Cybernetics, Part C, Volume 42, Issue 6, Pages 1491-1499, Nov. 2012.

  [136]  W. Li, D. Zhang, L. Zhang, G. Lu, and J. Yan, “3-D Palmprint Recognition with Joint Line and Orientation Features,” IEEE Trans. on System, Man and Cybernetics, Part C, vol. 41, No. 2, pp.274-279, April, 2011. (paper) (database)

  [137]  D. Zhang, G. Lu, W. Li, L. Zhang, and N. Luo, “Palmprint Recognition using 3-D Information,” IEEE Trans. System, Man and Cybernetics, Part C, vol. 39, no. 5, pp. 505-519, Sept. 2009. (paper) (database)

  [138]  L. Zhang and D. Zhang, “Characterization of palmprints by wavelet signatures via directional context modeling,” IEEE Trans. on System, Man and Cybernetic, Part B. vol. 34, pp. 1335-1347, June, 2004. (paper)

  [139]  Z. Guo, D. Zhang, L. Zhang, and W. Zuo, “Palmprint Verification using Binary Orientation Co-occurrence Vector,” Pattern Recognition Letters, vol. 30, no. 13, pp. 1219-1227, October, 2009. (paper)

  [140]  Z. Guo, W. Zuo, L. Zhang, and D. Zhang, “A Unified Distance Measurement for Orientation Coding in Palmprint Verification,”Neurocomputing, Volume 73, pp. 944-950, Issues 4-6, January 2010. (paper)

  [141]  D. Zhang, Z. Guo, G. Lu, L. Zhang, and W. Zuo, “An Online System of Multispectral Palmprint Verification,” IEEE Trans. on Instrument and Measurement, vol. 59, no. 2, pp. 480-490, Feb. 2010. (paper) (database)

  [142]  D. Zhang, Z. Guo, G. Lu, L. Zhang, Y. Liu, and W. Zuo, “Online Joint Palmprint and Palmvein Verification,” Expert System with Applications, vol. 38, issue 3, pp. 2621-2631, March 2011. (paper)

  [143]  Q. Zhao, D. Zhang, L. Zhang, and N. Luo, “Adaptive Fingerprint Pore Modeling and Extraction,” Pattern Recognition, Volume 43, Issue 8, Pages 2833-2844, August 2010. (paper) (database)

  [144]  Q. Zhao, D. Zhang, L. Zhang, and N. Luo, “High resolution partial fingerprint alignment using pore-valley descriptors”, Pattern Recognition, vol. 43, no. 3, pp. 1050-1061, Mar. 2010. (paper) (database)

  [145]  X. Zhu, L. Zhang, and Z. Huang, “A Sparse Embedding and Least Variance Encoding Approach to Hashing,” IEEE Trans. on Image Processing, vol. 23, issue 9, pp. 3737- 3750, Sept. 2014. (paper) (code)

  [146]  G. Liu, L. Zhang,Y. Hou, Z. Li, and J. Yang, “Image Retrieval Based on Multi-Texton Histogram,” Pattern Recognition, Volume 43, Issue 7,  pp. 2380-2389, July 2010. (paper, code)

  [147]  G. Liu, Z. Li, L. Zhang, and Y. Xu, “Image Retrieval based on Micro-structure Descriptor,” Pattern Recognition, vol. 44, issue 9, pp.2123-2133, September 2011. (paper) (code)

  [148]  W. Lian, L. Zhang, and D. Zhang, “Rotation Invariant Nonrigid Point Set Matching in Cluttered Scenes,” IEEE Trans. Image Processing, vol. 21, issue 5, pp. 2786-2797, May 2012. (paper, source code)

  [149]  W. Lian, L. Zhang, Y. Liang, and Q. Pan, “A Quadratic Programming based Cluster Correspondence Projection Algorithm for Fast Point Matching,” Computer Vision and Image Understanding, Vol. 114, Issue 3, pp. 322-333, March 2010. (paper, matlab code)

  [150]  B. Paul, L. Zhang, and X. Wu, “Canny edge detection enhancement by scale multiplication,” IEEE. Trans. on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1485-1490, Sept. 2005. (paper, matlab code)

  [151]  L. Zhang, B. Paul, and X. Wu, “Edge detection by scale multiplication in wavelet domain,” Pattern Recognition Letters, vol. 23, pp. 1771-1784, 2002. (paper)

  [152]  Xi Chen, Robert M. Nishikawa, Suk-tak Chan, Beverly A. Lau, L. Zhang, and X. Mou, “Algorithmic scatter correction in dual-energy digital mammography,” Medical Physics, vol. 40, 111919 (2013). DOI: http://dx.doi.org/10.1118/1.4826173

  [153]  Q. Xu, H. Yu. X. Mou, L. Zhang, H. Jiang, and G. Wang, “Low-dose X-ray CT Reconstruction via Dictionary Learning,” IEEE Transactions on Medical Imaging, Volume 31, Issue 9, Pages 1682-1697, Sept. 2012.

  [154]  B. Zhang, F. Karray, Q. Li, and L. Zhang, “Sparse Representation Classifier for Microaneurysm Detection and Retinal Blood Vessel Extraction,” Information Sciences, Volume 200, Pages 78-90, Oct. 2012.

  [155]  Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric Imaging for Pathological Analysis of Skin,” Applied Optics, vol. 48(10), pp. D236-D246, April 2009. (paper, dataset)

  [156]  X. Mou, X. Chen, L. Sun, H. Yu, Z. Ji, and L. Zhang, “The impact of calibration phantom errors on dual-energy digital mammography,”Phys. Med. Biol. 53 6321-6336, 2008. (paper)

  [157]  L. Zhang, Q. Li, J. You, and D. Zhang, “A Modified Matched Filter with Double-Sided Thresholding for Screening Proliferative Diabetic Retinopathy,” IEEE Trans. Information Technology in Biomedicine, vol. 13, no. 4, pp. 528-534, July 2009. (paper)

  [158]  Bob Zhang, Lin Zhang, Lei Zhang, and Fakhri Karray, “Retinal Vessel Extraction by Matched Filter with First-Order Derivative of Gaussian,” Computers in Biology and Medicine, Volume 40, Issue 4, April 2010, Pages 438-445. (paper, matlab code)

  [159]  D. Guo, D. Zhang, and L. Zhang, “Sparse representation-based classification for breath sample identification,” SENSORS AND ACTUATORS B-CHEMICAL, Vol. 158, No. 1, pp.43-53, 2011.

  [160]  D. Guo, D. Zhang, and L. Zhang, “An LDA Based Sensor Selection Approach Used in Breath Analysis System,” SENSORS AND ACTUATORS B-CHEMICAL, Vol. 157, No. 1, pp.265-274, 2011.

  [161]  D. Guo, D. Zhang, N. Li, L. Zhang, and J. Yang, “A Novel Breath Analysis System Based on Electronic Olfaction” IEEE Trans. on Biomedical Engineering, vol. 57, no. 11, pp. 2753-2763, Nov. 2010. (paper)

  [162]  Y. Chen, L. Zhang, D. Zhang, and D. Zhang, “Wrist Pulse Signal Diagnosis using Modified Gaussian Models and Fuzzy C-Means Classification,” Medical Engineering & Physics, Vol. 31, Issue 10, pp. 1283-1289, Dec. 2009. (paper)

  [163]  Y. Chen, L. Zhang, D. Zhang, and D. Zhang, “Computerized Wrist Pulse Signal Diagnosis Using Modified Auto-Regressive Models,”Journal of Medical Systems, Sept. 2009, DOI 10.1007/s10916-009-9368-4. (paper)

  [164]  B. Paul and L. Zhang “Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding,” IEEE Trans. on Medical Imaging, vol.22, pp. 1089-1099, Sep. 2003. (paper, matlab code)

  [165]  C. Zheng, L. Zhang, T. Ng, C. Shiu, and D. Huang, “Molecular Pattern Discovery Based on Penalized Matrix Decomposition,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 6, pp. 1592-1603, November/December 2011. (paper, code)

  [166]  C. Zheng, L. Zhang, T. Ng, and C. Shiu, “Metasample Based Sparse Representation for Tumor Classification,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, issue 5, pp. 1273 – 1282, Sept.-Oct. 2011. (paper, code)

  [167]  C. Zheng, V. Ng, L. Zhang, C. Shiu, and H. Wang, “Tumor Classification Based on Non-negative Matrix Factorization using Gene Expression Data,” IEEE Transactions on NanoBioscience, vol. 10, no. 2, pp. 86-93, June 2011.

  [168]  C. Zheng, D. Huang, L. Zhang, and X. Kong, “Tumor Clustering Using Nonnegative Matrix Factorization with Gene Selection,” IEEE Trans. Information Technology in Biomedicine, vol. 13, no. 4, pp.599-607, July 2009. (paper, code)

  [169]  Y. Liang, J. Cao, Lei Zhang, R. Wang, and Q. Pan, “A Biologically-Inspired Sensor Wakeup Control Method for Wireless Sensor Networks,” IEEE Trans. on System, Man and Cybernetics, Part C, vol. 40, issue 5, pp. 525–538, Sept. 2010. (paper)

  [170]  L. Zhang and X. Wu, “On the Application of Cross Correlation Function to Subsample Discrete Time Delay Estimation,” Digital Signal Processing, 16 (2006), pp. 682-694. (paper)

  [171]  L. Zhang, X. Wu, Q. Pan, and H. Zhang, “Multiresolution modeling and estimation of multisensor data,” IEEE Trans. on Signal Processing, vol. 52, pp. 3170-3182, Nov. 2004. (paper)

  [172]  L. Zhang, B. Paul, and X. Wu, “Wavelet estimation of fractional Brownian motion embedded in a noisy environment,” IEEE Trans. on Information Theory, vol. 50, pp. 2194-2200, Sept. 2004. (paper)

  [173]  L. Zhang, Q. Pan, B. Paul, and H. Zhang, “The Discrete Kalman Filtering of A Class of Dynamic Multiscale Systems,” IEEE Trans. on Circuits and Systems II: Digital and Analog Signal Processing, vol.49, pp. 668-676, Oct. 2002. (paper)

  [174]  D. Chen, S. Zhao, L. Zhang, Y. Yang, and X. Zhang, “Sample Pair Selection for Attribute Reduction with Rough Set,” IEEE Transactions on Knowledge and Data Engineering, Volume 24, Issue 11, Pages 2080-2093, Nov. 2012.

  [175]  D. Chen, L. Zhang, S. Zhao, Q. Hu, and P. Zhu, “A novel algorithm for finding reducts with fuzzy rough sets,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 2, pp. 385-389, 2012.

  [176]  Q. Hu, L. Zhang, S. An, D. Zhang, and D. Yu, “On robust fuzzy rough set models,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 4, pp. 636-651, 2012.

  [177]  Q. Hu, W. Pan, L. Zhang, D. Zhang, Y. Song, M. Guo, and D. Yu, “Feature selection for monotonic classification,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 1, pp. 69-81, 2012.

  [178]  Q. Hu, X. Che, L. Zhang, D. Zhang, M. Guo, and D. Yu, “Rank Entropy Based Decision Trees for Monotonic Classification,” IEEE Transactions on Knowledge and Data Engineering, Volume 24, Issue 11, Pages 2052-2064, Nov. 2012.

  [179]  Q. Hu, L. Zhang, D. Zhang, W. Pan, S. An, and W. Pedrycz, “Measuring relevance between discrete and continuous features based on neighborhood mutual information,” Expert Systems with Applications, vol. 38, no. 9, pp. 10737-10750, Sept. 2011.

  [180]  Q. Hu, L. Zhang, D. Chen, W. Pedrycz, and D. Yu, “Gaussian Kernel Based Fuzzy Rough Sets: Model, Uncertainty Measures and Applications,” International Journal of Approximate Reasoning, Volume 51, Issue 4, March 2010, Pages 453-471. (paper)

  [181]  Q. Hu, X. Chen, L. Zhang, and D. Yu, “Feature Evaluation and Selection Based on Neighborhood Soft Margin,” Neurocomputing, Volume 73, Issues 10-12, June 2010, Pages 2114-2124. (paper)

  [182]  J. Zhou, X. Wu, and L. Zhang, “ℓ2 Restoration of ℓ∞-Decoded Images via Soft-Decision Estimation,” IEEE Transactions on Image Processing, Volume 21, Issue 12, Pages 4797-4807, Dec. 2012.