Proceedings ArticleDOI
Similarity learning on an explicit polynomial kernel feature map for person re-identification
Dapeng Chen,Zejian Yuan,Gang Hua,Nanning Zheng,Jingdong Wang +4 more
- pp 1565-1573
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TLDR
This paper presents an explicit polynomial kernel feature map, which is capable of characterizing the similarity information of all pairs of patches between two images, called soft-patch-matching, instead of greedily keeping only the best matched patch, and thus more robust.Abstract:
In this paper, we address the person re-identification problem, discovering the correct matches for a probe person image from a set of gallery person images. We follow the learning-to-rank methodology and learn a similarity function to maximize the difference between the similarity scores of matched and unmatched images for a same person. We introduce at least three contributions to person re-identification. First, we present an explicit polynomial kernel feature map, which is capable of characterizing the similarity information of all pairs of patches between two images, called soft-patch-matching, instead of greedily keeping only the best matched patch, and thus more robust. Second, we introduce a mixture of linear similarity functions that is able to discover different soft-patch-matching patterns. Last, we introduce a negative semi-definite regularization over a subset of the weights in the similarity function, which is motivated by the connection between explicit polynomial kernel feature map and the Mahalanobis distance, as well as the sparsity constraint over the parameters to avoid over-fitting. Experimental results over three public benchmarks demonstrate the superiority of our approach.read more
Citations
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Proceedings ArticleDOI
Scalable Person Re-identification: A Benchmark
TL;DR: A minor contribution, inspired by recent advances in large-scale image search, an unsupervised Bag-of-Words descriptor is proposed that yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large- scale 500k dataset.
Posted Content
Person Re-identification: Past, Present and Future
TL;DR: The history of person re-identification and its relationship with image classification and instance retrieval is introduced and two new re-ID tasks which are much closer to real-world applications are described and discussed.
Book ChapterDOI
MARS: A Video Benchmark for Large-Scale Person Re-Identification
TL;DR: It is shown that CNN in classification mode can be trained from scratch using the consecutive bounding boxes of each identity, and the learned CNN embedding outperforms other competing methods considerably and has good generalization ability on other video re-id datasets upon fine-tuning.
Proceedings ArticleDOI
Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion
TL;DR: This study proposes a novel Convolutional Neural Network, called Spindle Net, based on human body region guided multi-stage feature decomposition and tree-structured competitive feature fusion, which is the first time human body structure information is considered in a CNN framework to facilitate feature learning.
Proceedings ArticleDOI
Pose-Driven Deep Convolutional Model for Person Re-identification
TL;DR: Zhang et al. as mentioned in this paper proposed a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end, which explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.
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