Divide and Fuse: A Re-ranking Approach for Person Re-identification

As re-ranking is a necessary procedure to boost person re-identification (re-ID) performance on large-scale datasets, the diversity of feature becomes crucial to person reID for its importance both on designing pedestrian descriptions and re-ranking based on feature fusion. However, in many circumstances, only one type of pedestrian feature is available. In this paper, we propose a "Divide and use" re-ranking framework for person re-ID. It exploits the diversity from different parts of a high-dimensional feature vector for fusion-based re-ranking, while no other features are accessible. Specifically, given an image, the extracted feature is divided into sub-features. Then the contextual information of each sub-feature is iteratively encoded into a new feature. Finally, the new features from the same image are fused into one vector for re-ranking. Experimental results on two person re-ID benchmarks demonstrate the effectiveness of the proposed framework. Especially, our method outperforms the state-of-the-art on the Market-1501 dataset.

[1]  Ming Yang,et al.  Query Specific Rank Fusion for Image Retrieval , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Song Bai,et al.  Sparse Contextual Activation for Efficient Visual Re-Ranking , 2016, IEEE Transactions on Image Processing.

[3]  Qi Tian,et al.  Scalable Person Re-identification: A Benchmark , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[4]  Amit K. Roy-Chowdhury,et al.  Temporal Model Adaptation for Person Re-identification , 2016, ECCV.

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

[6]  Jian-Huang Lai,et al.  Person Re-Identification by Camera Correlation Aware Feature Augmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Vittorio Murino,et al.  Custom Pictorial Structures for Re-identification , 2011, BMVC.

[8]  Takahiro Okabe,et al.  Hierarchical Gaussian Descriptor for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  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).

[10]  Cordelia Schmid,et al.  A contextual dissimilarity measure for accurate and efficient image search , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Qi Tian,et al.  MARS: A Video Benchmark for Large-Scale Person Re-Identification , 2016, ECCV.

[12]  Shaogang Gong,et al.  Learning a Discriminative Null Space for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Nanning Zheng,et al.  Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[15]  Ying Wu,et al.  Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Nanning Zheng,et al.  Similarity learning on an explicit polynomial kernel feature map for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Bingpeng Ma,et al.  BiCov: a novel image representation for person re-identification and face verification , 2012, BMVC.

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

[19]  Zhen Li,et al.  Learning Locally-Adaptive Decision Functions for Person Verification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Zheng Wang,et al.  Person Reidentification via Ranking Aggregation of Similarity Pulling and Dissimilarity Pushing , 2016, IEEE Transactions on Multimedia.

[21]  Masayuki Mukunoki,et al.  Common-near-neighbor analysis for person re-identification , 2012, 2012 19th IEEE International Conference on Image Processing.

[22]  Qi Tian,et al.  Regularized Diffusion Process for Visual Retrieval , 2017, AAAI.

[23]  Nanning Zheng,et al.  Similarity Learning with Spatial Constraints for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[25]  Gang Wang,et al.  Gated Siamese Convolutional Neural Network Architecture for Human Re-identification , 2016, ECCV.

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

[27]  Anurag Mittal,et al.  Deep Neural Networks with Inexact Matching for Person Re-Identification , 2016, NIPS.

[28]  Yi Yang,et al.  Person Re-identification: Past, Present and Future , 2016, ArXiv.

[29]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[31]  Shengcai Liao,et al.  Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[32]  Qi Tian,et al.  Query-adaptive late fusion for image search and person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Hai Tao,et al.  Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features , 2008, ECCV.

[34]  Qi Tian,et al.  Scalable Person Re-identification on Supervised Smoothed Manifold , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Shiliang Zhang,et al.  Deep Attributes Driven Multi-Camera Person Re-identification , 2016, ECCV.

[36]  Qi Tian,et al.  Multimedia search reranking: A literature survey , 2014, CSUR.

[37]  Alfredo Gardel Vicente,et al.  Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[38]  Yimin Wang,et al.  Person re-identification with content and context re-ranking , 2015, Multimedia Tools and Applications.

[39]  Gang Wang,et al.  A Siamese Long Short-Term Memory Architecture for Human Re-identification , 2016, ECCV.

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