Discriminative deep transfer metric learning for cross-scenario person re-identification

Abstract. A discriminative deep transfer metric learning method called DDTML is proposed for cross-scenario person re-identification (Re-ID). To develop the Re-ID model in a new scenario, a large number of pairwise cross-camera-view person images are deemed necessary. However, this work is very expensive due to both monetary cost and labeling time. In order to solve this problem, a DDTML for cross-scenario Re-ID is proposed using the transferring data in other scenarios to help build a Re-ID model in a new scenario. Specifically, to measure distribution difference across scenarios, a maximum mean discrepancy based on class distribution called MMDCD is proposed by embedding the discriminative information of data into the concept of the maximum mean discrepancy. Unlike most metric learning methods, which usually learn a linear distance to project data into the feature space, DDTML uses a deep neural network to develop the multilayers nonlinear transformations for learning the nonlinear distance metric, while DDTML transfers discriminative information from the source domain to the target domain. By bedding the MMDCD criteria, DDTML minimizes the distribution divergence between the source domain and the target domain. Experimental results on widely used Re-ID datasets show the effectiveness of the proposed classifiers.

[1]  Masashi Sugiyama,et al.  Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis , 2007, J. Mach. Learn. Res..

[2]  Shengcai Liao,et al.  Person re-identification by Local Maximal Occurrence representation and metric learning , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Pong C. Yuen,et al.  Dynamic Label Graph Matching for Unsupervised Video Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[4]  Jiwen Lu,et al.  Discriminative Deep Metric Learning for Face Verification in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Liang Zheng,et al.  Re-ranking Person Re-identification with k-Reciprocal Encoding , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[7]  Vittorio Murino,et al.  Symmetry-driven accumulation of local features for human characterization and re-identification , 2013, Comput. Vis. Image Underst..

[8]  Ehud Rivlin,et al.  Color Invariants for Person Reidentification , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Shaogang Gong,et al.  Reidentification by Relative Distance Comparison , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Shaogang Gong,et al.  Unsupervised Cross-Dataset Transfer Learning for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Jie Wang,et al.  Person re-identification by multiple instance metric learning with impostor rejection , 2017, Pattern Recognit..

[12]  Horst Bischof,et al.  Mahalanobis Distance Learning for Person Re-identification , 2014, Person Re-Identification.

[13]  Qiang Yang,et al.  Transfer Learning via Dimensionality Reduction , 2008, AAAI.

[14]  Chunxiao Liu,et al.  Person re-identification by manifold ranking , 2013, 2013 IEEE International Conference on Image Processing.

[15]  Abhir Bhalerao,et al.  Person reidentification using deep foreground appearance modeling , 2018, J. Electronic Imaging.

[16]  Jenq-Neng Hwang,et al.  Normalized distance aggregation of discriminative features for person reidentification , 2018, J. Electronic Imaging.

[17]  Sergio A. Velastin,et al.  Local Fisher Discriminant Analysis for Pedestrian Re-identification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Alessandro Perina,et al.  Person re-identification by symmetry-driven accumulation of local features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Bir Bhanu,et al.  Person Reidentification With Reference Descriptor , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  Larry S. Davis,et al.  Attributes driven tracklet-to-tracklet person re-identification using latent prototypes space mapping , 2017, Pattern Recognit..

[21]  Xiang Li,et al.  Partial Person Re-Identification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[22]  Bir Bhanu,et al.  Person Re-Identification by Robust Canonical Correlation Analysis , 2015, IEEE Signal Processing Letters.

[23]  Shengcai Liao,et al.  Salient Color Names for Person Re-identification , 2014, ECCV.

[24]  Nanning Zheng,et al.  Point to Set Similarity Based Deep Feature Learning for Person Re-Identification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Kenji Mase,et al.  People re-identification using two-stage transfer metric learning , 2015, 2015 14th IAPR International Conference on Machine Vision Applications (MVA).

[26]  Inderjit S. Dhillon,et al.  Information-theoretic metric learning , 2006, ICML '07.

[27]  Cordelia Schmid,et al.  Is that you? Metric learning approaches for face identification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[28]  Kaizhu Huang,et al.  Geometry preserving multi-task metric learning , 2012, Machine Learning.

[29]  Jiwen Lu,et al.  Multi-modal uniform deep learning for RGB-D person re-identification , 2017, Pattern Recognit..

[30]  Shaogang Gong,et al.  Person Re-Identification by Unsupervised Video Matching , 2016, Pattern Recognit..

[31]  Horst Bischof,et al.  Large scale metric learning from equivalence constraints , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Jian Sun,et al.  Face recognition with learning-based descriptor , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[33]  Xiang Li,et al.  Cross-Scenario Transfer Person Reidentification , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[34]  Xiaogang Wang,et al.  Locally Aligned Feature Transforms across Views , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.