Person Re-identification by Salience Matching

By Rui Zhao, Wanli Ouyang, Xiaogang Wang. 2013

498 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yi Yang
2. Xiaogang Wang
3. Liang Zheng
4. Qi Tian
5. Luc Van Gool
6. Dacheng Tao
7. Ming-Hsuan Yang
8. Bernt Schiele

Citations in the corpus

• Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Like other areas of computer vision, the last years have seen a shift from classifiers based on handcrafted features and metric learning approaches [13, 14, 15, 16, 17, 18, 19] towards methods based on deep learning [20, 21, 22, 23, 24, 25, 26, 27].”
• Deeply-Learned Part-Aligned Representations for Person Re-identification (2017, unknown): “SalMatch [60] (ICCV13) 28.”
• Cross Dataset Person Re-identification (2014, background): “Salience were gradually applied in person re-identification as well [21–23].”
• Attribute-Aware Attention Model for Fine-grained Representation Learning (2018, unknown): “Non-deep learning methods extract hand-crafted features [20, 51], especially color and texture descriptors, then use distance metric learning [14, 20] to further capture discriminative factors.”
• Attention-Aware Compositional Network for Person Re-identification (2018, background): “of features, namely global features extracted from the whole image [11, 44] and region features generated from local patches [51, 57, 22].”
• Deep feature learning with relative distance comparison for person re-identification (2015, methodology): “Comparison on VIPeR dataset Using the VIPeR dataset, we compared our method with such state-of-the-art methods as MtMCML [30], LMLF [13], SDALF [5], eBiCov [31], eSDC [32], PRDC [9], aPRDC [33], PCCA [14], KISSME [34], LF [35] and SalMatch [36].”
• Partial Person Re-Identification (2015, background): “Person re-identification (re-id) has been studied extensively in the past five years [5, 30, 4, 37, 10, 20, 36].”
• Person Re-Identification Using Kernel-Based Metric Learning Methods (2014, result): “For sake of completeness, we also compared the best performance for the proposed algorithms against the best results as reported in the existing literature (even though as pointed above, the values reported elsewhere do not use the same set of features or experimental protocol) [1, 2, 6, 11,16–21,28”
• Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification (2015, background): “Therefore, 1550-5499/15 $31.00 © 2015 IEEE DOI 10.1109/ICCV.2015.420 3685 the learning process can be dominated by a large number of negative sample pairs, making the limited positive pairs easily be neglected and resulting in a weak metric.”
• Salient Color Names for Person Re-identification (2014, background): “[30] exploit salience matching, which is tightly integrated with patch matching in a unified structural RankSVM learning framework, to match persons over disjoint camera views.”
• Hierarchical Gaussian Descriptor for Person Re-identification (2016, methodology): “Considering other methods which require more computational cost [6, 45, 47], the running time of the GOG descriptor is still appealing.”
• Unsupervised Person Re-identification (2017, background): “Gray and Tao [28] use 8 color channels and 21 texture filters on the luminance channel, and the pedestrian is partitioned into horizontal stripes.”
• Person Re-Identification by Camera Correlation Aware Feature Augmentation (2017, unknown): “1 SalMatch [86] 30.”
• Person Recognition in Personal Photo Collections (2017, background): “There is an active line of research on pedestrian re-id, starting with hand-crafted features [30], [31], [32] and has moved towards deep feature based schemes [28], [33], [34], [35], [36], [37], [38], [39], [40].”
• Scalable Person Re-identification on Supervised Smoothed Manifold (2017, unknown): “36 SalMatch [60] ICCV2013 30.”
• Scalable Person Re-identification: A Benchmark (2015, methodology): “Nevertheless, some state-of-the-art methods in person re-identification rely on brute-force featurefeature matching [39, 38].”