Abstract To solve the over-fitting problem caused by less training samples in the current person re-identification method based on distance metric learning, a person re-identification algorithm based on regularization of independent measure matrix is proposed. Firstly, the features extracted from four different color spaces are used to learn four different measure matrices. Then, the corresponding matrixes are regularized respectively, and the similarity of testing examples is measured by the regularized matrices. Finally, the final similarity is obtained by fusing results of the similarity measure. Experimental results show the improvement of the proposed method in performance for the over-fitting problem caused by less training samples.
Corresponding Authors:
WANG Yunxia(Corresponding author), born in 1991, master student. Her research interests include person re-identification.
About author:: QI Minbin, born in 1969, Ph.D., professor. His research interests include intelligent video surveillance and DSP technology and its application.(TAN Shengshun, born in 1990, master student. His research interests include object retrieval.(LIU Hao, born in 1988, Ph. D. candidate. His research interests include machine learning.JIANG Jiangguo, born in 1955, Ph.D., professor. His research interests include intelligent video surveillance, image processing, DSP technology and its application.
[1] GONG S G, CRISTANI M, YAN S C, et al. Person Re-identification. London, UK: Springer, 2014. [2] BALTIERI D, VEZZANI R, CUCCHIARA R. 3DPeS: 3D People Dataset for Surveillance and Forensics // Proc of the Joint ACM Workshop on Human Gesture and Behavior Understanding. New York, USA: ACM, 2011: 59-64. [3] SWAIN M J, BALLARD D H. Indexing via Color Histograms // Proc of the 3rd International Conference on Computer Vision. Osaka, Japan: IEEE, 1990: 390-393. [4] 范彩霞,朱 虹,蔺广逢,等.多特征融合的人体目标再识别.中国图象图形学报, 2013, 18(6): 711-717. (FAN C X, ZHU H, LIN G F, et al. Person Re-identification Based on Multi-features. Journal of Image and Graphics, 2013, 18(6): 711-717.) [5] TUZEL O, PORIKLI F, MEER P. Region Covariance: A Fast Descriptor for Detection and Classification // Proc of the 9th European Conference on Computer Vision. Berlin, Germany: Springer-Verlag, 2006: 589-600. [6] HIRZER M, BELEZNAI C, ROTH P M, et al. Person Re-identification by Descriptive and Discriminative Classification // Proc of the 17th Scandinavian Conference on Image Analysis. Berlin, Germany: Springer-Verlag, 2011: 91-102. [7] YANG Y, YANG J M, YAN J J, et al. Salient Color Names for Person Re-identification // Proc of the 13th European Conference on Computer Vision. Zurich, Switzerland: Springer International Publishing, 2014: 536-551. [8] MARTINEL N, MICHELONI C, FORESTI G L. Saliency Weighted Features for Person Re-identification // Proc of the 13th European Conference on Computer Vision. Zurich, Switzerland: Springer International Publishing, 2014, I: 191-208. [9] ZHAO R, OUYANG W L, WANG X G. Learning Mid-level Filters for Person Re-identification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE, 2014: 144-151. [10] KSTINGER M, HIRZER M, WOHLHART P, et al. Large Scale Metric Learning from Equivalence Constraints // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012: 2288-2295. [11] WEINBERGER K Q, SAUL L K. Fast Solvers and Efficient Implementations for Distance Metric Learning // Proc of the 25th International Conference on Machine Learning. New York, USA: ACM, 2008: 1160-1167. [12] DAVIS J V, KULIS B, JAIN P. Information-Theoretic Metric Learning // Proc of the 24th International Conference on Machine Learning. Corvallis, USA: IEEE, 2007: 209-216. [13] DIKMEN M, AKBAS E, HUANG T S, et al. Pedestrian Recognition with a Learned Metric // Proc of the 10th Asian Conference on Computer Vision. Berlin, Germany: Springer-Verlag, 2010, IV: 501-512. [14] WEINBERGER K Q, SAUL L K. Distance Metric Learning for Large Margin Nearest Neighbor Classification. Journal of Machine Learning Research, 2009, 10: 207-244. [15] GUILLAUMIN M, VERBEEK J, SCHMID C. Is That You? Metric Learning Approaches for Face Identification // Proc of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009: 498-505. [16] 杜宇宁,艾海舟.基于统计推断的行人再识别算法.电子与信息学报, 2014, 36(7): 1612-1618. (DU Y N, AI H Z. A Statistical Inference Approach for Person Re-identification. Journal of Electronics & Information Technology, 2014, 36(7): 1612-1618.) [17] MA L Y, YANG X K, TAO D C. Person Re-identification over Camera Networks Using Multi-task Distance Metric Learning. IEEE Trans on Image Processing, 2014, 23(8): 3656-3670. [18] LIU W F, TAO D C, CHENG J, et al. Multiview Hessian Discriminative Sparse Coding for Image Annotation. Computer Vision and Image Understanding, 2014, 118: 50-60. [19] LIU W F, LIU H L, TAO D P, et al. Multiview Hessian Regula-rized Logistic Regression for Action Recognition. Signal Processing, 2015, 110: 101-107. [20] TAO D P, JIN L W, WANG Y F, et al. Person Re-identification by Regularized Smoothing KISS Metric Learning. IEEE Trans on Circuits and Systems for Video Technology, 2013, 23(10): 1675-1685. [21] GEVERS T, SMEULDERS A W M. Color-Based Object Recognition. Pattern Recognition, 1999, 32: 453-464. [22] GRAY D, BRENNAN S, TAO H. Evaluating Appearance Models for Recognition, Reacquisition, and Tracking [EB/OL]. [2015-07-25]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.331.7285&rep=rep1&type=pdf. [23] ZHENG W S, GONG S G, XIANG T. Associating Groups of People // Proc of the British Machine Vision Conference. London, UK: BMVA Press, 2009. DOI:10.5244/C.23.23.
2016, Vol. 29 
