Salient Color Names for Person Re-identification

By Yang Yang, Jimei Yang, Junjie Yan, Shengcai Liao, Dong Yi, S. Li. European Conference on Computer Vision, 2014

550 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/731403fcccb6cc740798c4155ffea1ddb9e69e7d

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. Liang Zheng
3. Xiaogang Wang
4. Qi Tian
5. Luc Van Gool
6. Dacheng Tao
7. Ming-Hsuan Yang
8. Tao A. Xiang

In Electrical and Electronic Engineering

1. Tianzhu Zhang
2. Yonghong Tian
3. Tao Mei
4. Ling Shao
5. Yihong Gong
6. Shaogang Gong
7. Qi Tian
8. Changsheng Xu

Citations in the corpus

• Hierarchical Gaussian Descriptor for Person Re-identification (2016, methodology): “State-of-the-arts: In Table 2, we compare the performance of the reported results on the state-of-the-art methods, including MidLevel Filter Learning (MLFL) [47], Salience Matching (SalMatch) [45] , SCNCD [43], Semantic attribute representation [38], Metric Ensemble [31]”
• Adaptive Transfer Network for Cross-Domain Person Re-Identification (2019, background): “Person re-identification is the task of matching a probe pedestrian image from a large-scale gallery collected by non-overlapping camera networks at diverse locations [17, 35, 18].”
• Gated Siamese Convolutional Neural Network Architecture for Human Re-identification (2016, background): “Scale Invariant Feature Transforms [28,56,57], Scale Invariant Local Ternary Patterns [24, 26], Local Binary Patterns [31, 49], Color Histograms [24,49,56,57] or Color Names [51,60] etc. are the basis of the majority of these feature representations developed for human re-identification.”
• A Siamese Long Short-Term Memory Architecture for Human Re-identification (2016, methodology): “Most human re-identification works concentrate on developing a feature representation [33,58,59,63] or learning a distance metric [31,33,57].”
• Deep Transfer Learning for Person Re-Identification (2016, background): “Despite the best efforts from the computer vision researchers, it remains an unsolved problem [1].”
• Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification (2017, unknown): “[7, 10, 18, 19, 22, 27, 41–43, 50, 51], or learning an effective distance metric [2, 16, 21, 22, 29, 47, 57].”
• Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-identification (2017, methodology): “Existing methods of person re-identification typically focus on designing invariant and discriminant features [24,22,50,38,100,46,87,10,43], which can enable identification despite nuisance factors such as scale, location, partial occlusion and changing lighting conditions.”
• Person re-identification by Local Maximal Occurrence representation and metric learning (2014, methodology): “Four methods, the SCNCD [44], kBiCov [33], LADF [24], and SalMatch [46] report the best performances on the VIPeR”
• Embedding Deep Metric for Person Re-identification: A Study Against Large Variations (2016, methodology): “The traditional methods work at improving suitable hand-crafted features [34,41], or good metric for comparison [26,13,15,18,23,39,36,38], or both of them [12,19,33,37].”
• Deep Learning for Person Re-Identification: A Survey and Outlook (2020, background): “…[14], unconstrained poses [15], [16], [17], occlusions [18], [19], heterogeneous modalities [9], [20], etc. Early research efforts mainly focus on the hand-crafted feature construction with body structures [4], [21], [22], [23], [24] or distance metric learning [25], [26], [27], [28], [29], [30].”
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification (2020, methodology): “Existing methods can be grouped into hand-crafted descriptors [25, 39, 18], metric learning methods [44, 16, 19] and deep learning algorithms [43, 11, 35, 36, 37, 22].”
• Person Re-identification by Deep Learning Multi-scale Representations (2017, background): “Many different handcrafted person image feature descriptors [13, 74, 61, 35, 70, 38, 29] have been designed in the past decade.”
• Learning a Discriminative Null Space for Person Re-identification (2016, methodology): “The first group of methods design invariant and discriminant features [20, 12, 6, 18, 26, 41, 24, 38, 22].”
• Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification (2015, unknown): “40 2014 ECCV [29] Kernel Ensb 2 36.”
• Person Re-Identification by Camera Correlation Aware Feature Augmentation (2017, background): “In current person re-id literature, the best performers are discriminative learning basedmethods [2], [3], [4], [5], [6], [7],”
• Group-aware Label Transfer for Domain Adaptive Person Re-identification (2021, background): “cations such as automatic surveillance, content-based retrieval, and behavior analysis [17, 32, 39, 5].”
• An improved deep learning architecture for person re-identification (2015, methodology): “Features that have been used include variations on color histograms [25, 11, 12, 20, 14, 29], local binary patterns [25, 11, 12, 20, 14], Gabor features [14], color names [26], and local patches [28].”