[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, “

       [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) () (An "opinion-unaware" method which outperforms all "opinion-aware" methods!)

       [5]       W. Zuo

       [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) ()

    [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. ()(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) ()(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) ()(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) ()

    [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)

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

    [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)

    [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)

    [60]    Lin Zhang, Lei Zhang, and David Zhang, “,” The 13th International Conference on Computer Analysis of Images and Patterns (CAIP09).

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

    [62]    Wei Li, Lei Zhang, and David Zhang, “,” 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, “,” 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, “,” 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, “,” 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) () (code

    [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) () (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.  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. code, )(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.  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.

    [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. 

    [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.  

    [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.  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

    [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.  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) () (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. 

    [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

    [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)

    [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. 

  [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.

  [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) ()(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. 

  [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. 

  [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. 

  [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.  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.

  [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. 

  [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) () (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)