A coarse-to-fine deep learning for person re-identification

This paper proposes a novel deep learning architecture for person re-identification. The proposed network is based on a coarse-to-fine learning (CFL) approach, attempting to acquire a generic-to-specific knowledge throughout a transfer learning process. The core of the method relies on a hybrid network composed of a convolutional neural network and a deep belief network denoising autoenconder. This hybrid network is in charge of extracting features invariant to illumination varying, certain image deformations, horizontal mirroring and image blurring, and is embedded in the CFL architecture. The proposed network achieved the best results when compared with other state-of-the-arts methods on i-LIDS, CUHK01 and CUHK03 data sets, and also a competitive performance on VIPeR data set.

[1]  Yunhong Wang,et al.  Relevance Metric Learning for Person Re-Identification by Exploiting Listwise Similarities , 2015, IEEE Transactions on Image Processing.

[2]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[3]  Stan Z. Li,et al.  Deep Metric Learning for Practical Person Re-Identification , 2014, ArXiv.

[4]  Xiaogang Wang,et al.  Deep Learning Identity-Preserving Face Space , 2013, 2013 IEEE International Conference on Computer Vision.

[5]  Fei Xiong,et al.  Person Re-Identification Using Kernel-Based Metric Learning Methods , 2014, ECCV.

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

[7]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .

[8]  Michael Jones,et al.  An improved deep learning architecture for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Pong C. Yuen,et al.  Domain Transfer Support Vector Ranking for Person Re-identification without Target Camera Label Information , 2013, 2013 IEEE International Conference on Computer Vision.

[10]  Xiaogang Wang,et al.  DeepReID: Deep Filter Pairing Neural Network for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[12]  Zheng Liu,et al.  Integrated Imaging and Vision Techniques for Industrial Inspection: Advances and Applications , 2015 .

[13]  LinLiang,et al.  Deep feature learning with relative distance comparison for person re-identification , 2015 .

[14]  Sameh Khamis,et al.  Person re-identification using semantic color names and RankBoost , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[15]  Horst Bischof,et al.  Relaxed Pairwise Learned Metric for Person Re-identification , 2012, ECCV.

[16]  Gian Luca Foresti,et al.  Kernelized Saliency-Based Person Re-Identification Through Multiple Metric Learning , 2015, IEEE Transactions on Image Processing.

[17]  Xiaogang Wang,et al.  Human Reidentification with Transferred Metric Learning , 2012, ACCV.

[18]  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).

[19]  Xiaogang Wang,et al.  Hierarchical face parsing via deep learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Jürgen Schmidhuber,et al.  Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.

[21]  Amir Globerson,et al.  Metric Learning by Collapsing Classes , 2005, NIPS.

[22]  Anton van den Hengel,et al.  Learning to rank in person re-identification with metric ensembles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[24]  Xiaogang Wang,et al.  Multi-stage Contextual Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[25]  Shishir K. Shah,et al.  A survey of approaches and trends in person re-identification , 2014, Image Vis. Comput..

[26]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[27]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[28]  Liang Lin,et al.  Deep feature learning with relative distance comparison for person re-identification , 2015, Pattern Recognit..

[29]  Geoffrey E. Hinton,et al.  Using very deep autoencoders for content-based image retrieval , 2011, ESANN.