Local Fisher Discriminant Analysis for Pedestrian Re-identification

By Sateesh Pedagadi, James Orwell, Sergio A. Velastin, Boghos A. Boghossian. 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

590 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/632cf1e4f51c63435cfbb9d109a4097385e35223

How researchers see this paper

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

In Biomedical Engineering

1. Liang Zheng
2. Yi Yang
3. Qi Tian
4. Shaogang Gong
5. Shiliang Zhang
6. Tao A. Xiang
7. Xiatian Zhu
8. Xiaogang Wang

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. Tao A. Xiang
8. Wanli Ouyang

In Safety, Risk, Reliability and Quality

1. Liang Zheng
2. Yi Yang
3. Shaogang Gong
4. Qi Tian
5. Tao A. Xiang
6. Mario Sznaier
7. Octavia I. Camps
8. Junjie Yan

Citations in the corpus

• Gated Siamese Convolutional Neural Network Architecture for Human Re-identification (2016, unknown): “LFDA [33] 24.”
• Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification (2018, background): “Image-based person re-identification mainly focuses on two categories: extracting discriminative features [13, 9, 33, 19, 43] and learning robust metrics [37, 50, 18, 36, 2].”
• Embedding Deep Metric for Person Re-identification: A Study Against Large Variations (2016, methodology): “We compare our model with IDLA [1], DeepFeature [6], visual word (visWord) [37], saliency matching (SalMatch), patch matching (PatMatch) [39], ELF [8], PRSVM [3], LMNNR [2], eBiCov [21], local Fisher discriminant analysis (LF) [28], PRDC [43], aPRDC [20], PCCA [25], mid-level filters (mFilter) [41] ”
• Saliency Weighted Features for Person Re-identification (2014, methodology): “[25,33,44,50]) learn non-Euclidean distances that are used for the classification phase.”
• PersonNet: Person Re-identification with Deep Convolutional Neural Networks (2016, methodology): “By applying these metric learning methods into person re-id, many effective approaches are developed [6], [8], [9], [13]–[17], [36].”
• Salient Color Names for Person Re-identification (2014, methodology): “Similar to [19,11], we repeat 100 trials of evaluation and report the average result to achieve a more stable results in the following.”
• Distilled Person Re-Identification: Towards a More Scalable System (2019, background): “Person Reidentification has witnessed a fast growing development in recent years, from feature design [19, 15, 32, 31, 35, 69, 58] to distance metric learning [54, 19, 43, 25, 67, 36, 41, 30, 58, 33, 38, 31, 9, 68, 60, 63, 69, 29, 52, 6] and end-to-end deep learning [28, 1, 55, 57, 49, 22, 64, 65, 7”
• Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification (2016, methodology): “We report the CMC curves and rank−1 accuracies of local Fisher discriminant analysis (LF) [29], pairwise constrained component analysis (PC− CA) [28], aPRDC [23], PRDC [43], enriched BiCov (eBi− Cov) [25], PRSVM [2], and ELF [10], saliency matching (SalMatch) [40], patch matching (PatMatch) [40], lo”
• Deep Attributes Driven Multi-Camera Person Re-identification (2016, background): “Currently, research on this topic mainly focus on two aspects: a) extracting and coding local invariant features to represent the visual appearance of a person [1,2,3,4,5,6] and b) learning a discriminative distance metric hence the distance of features from the same person can be smaller [7,8,9,10,”
• Deep feature learning with relative distance comparison for person re-identification (2015, methodology): “[9], aPRDC [33], PCCA [14], KISSME [34], LF [35] and SalMatch [36].”
• Scalable Person Re-identification on Supervised Smoothed Manifold (2017, unknown): “Local Fisher [39] CVPR2013 24.”
• An improved deep learning architecture for person re-identification (2015, methodology): “1, we compare our method against local Fisher discriminant analysis (LF) [23], PCCA [22], aPRDC [18], PRDC [32], eBiCov [19], LMNNR [1], PRSVM [2], and ELF [7].”
• Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification (2015, background): “[18], salience match [30], mid-level filter [32], local fisher discriminant analysis (LF) [22], to name a few.”
• Deep Representation Learning With Part Loss for Person Re-Identification (2017, background): “Existing approaches conquer this challenge by either seeking discriminative metrics [1], [2], [3], [4], [5], [6], [7], [8], [2], [9], [10], [11], or generating discriminative features [12], [13], [14], [15], [16], [17], [18], [19].”
• Pose-Driven Deep Convolutional Model for Person Re-identification (2017, background): “them [11, 6, 33, 29, 60, 51, 64]; (b) closing the gap between a person’s different features by learning a discriminative distance metric [32, 9, 17, 36, 55, 23, 54, 30, 26, 65, 50, 3, 27, 4, 39, 28, 10, 37, 59].”
• Person Re-identification by Deep Learning Multi-scale Representations (2017, background): “They have achieved a sequence of continuous re-id performance boost on benchmarking datasets when integrated with various supervised matching models [22, 41, 75, 28, 68, 29, 30, 40, 72, 63, 73, 62, 80, 42, 71].”
• Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification (2017, background): “For similarity measurement, many metric learning approaches are proposed to learn a suitable metric, such as locally adaptive decision functions [20], local fisher discriminant analysis [25], cross-view quadratic discriminant analysis [21], and etc.”