Color Invariants for Person Reidentification

By Igor Kviatkovsky, Amit Adam, Ehud Rivlin. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013

365 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9d3b5dbb0244b6ab733dac72a4f5f2fb9f1b55bd

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. Xiatian Zhu
6. Tao A. Xiang
7. G. Wang
8. Weishi Zheng

In Computer Vision and Pattern Recognition

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

Citations in the corpus

• Cross-Modality Person Re-Identification with Generative Adversarial Training (2018, background): “The latter is to learn invariant feature directly, upon which efficient L2 or Cosine distance can be applied [Farenzena et al. , 2010; Kviatkovsky et al. , 2013; Zhao et al. , 2013; Yang et al. , 2014; Liao et al. , 2015; Matsukawa et al. , 2016; Ma et al. , 2017 ] .”
• Deep Transfer Learning for Person Re-Identification (2016, background): “Earlier works focus on either designing viewinsensitive feature representations [2], [3], or learning an effective distance metric [4], [5], or both [6].”
• Learning a Discriminative Null Space for Person Re-identification (2016, background): “The first group of methods design invariant and discriminant features [20, 12, 6, 18, 26, 41, 24, 38, 22].”
• Multi-scale Deep Learning Architectures for Person Re-identification (2017, background): “They focus on either designing cross-view robust features [7, 13, 26, 62, 32], or learning robust distance metrics [31, 33, 63, 25, 49, 60, 38, 32], or both [24, 32, 57, 59].”
• Enhancing Person Re-identification in a Self-Trained Subspace (2017, methodology): “Earlier works include fisher vector based local descriptors [29], color invariant features [30] and saliency learning based methods [28].”
• Recurrent Convolutional Network for Video-Based Person Re-identification (2016, background): “The first of these employs invariant feature based methods that attempt to extract features that are both discriminative and invariant to environmental and view-point changes [23, 3, 6].”
• Deep Metric Learning for Practical Person Re-Identification (2014, methodology): “The recent advance in this aspect is the color invariant signature [13].”
• A Siamese Long Short-Term Memory Architecture for Human Re-identification (2016, background): “Most of the works on human re-identification concentrates on either developing a new feature representation [9, 27, 33, 36, 58, 59, 63] or learning a new distance”
• 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].”
• Gated Siamese Convolutional Neural Network Architecture for Human Re-identification (2016, background): “Existing research on human re-identification mainly concentrates on two aspects: (1) Developing a new feature representation [5, 19, 24, 29, 44, 50, 51, 55] and (2) Learning a distance metric [20, 23, 24, 25, 32, 34, 41, 49].”
• Recurrent Attention Models for Depth-Based Person Identification (2016, background): “There have been many attempts to solve this problem by improving the feature representations [24, 68, 22, 78, 79, 37, 80] and by exploring new similarity metrics [40, 49, 60].”
• Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification (2017, neutral): “[7, 10, 18, 19, 22, 27, 41–43, 50, 51], or learning an effective distance metric [2, 16, 21, 22, 29, 47, 57].”
• Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification (2017, background): “tation learning [11, 19, 15, 38], distance metric learning [14, 41, 30, 36, 37, 21] and CNN-based schemes [33, 32, 24, 25].”
• Salient Color Names for Person Re-identification (2014, methodology): “[13] propose a novel illumination-invariant feature representation based on logchromaticity (log) color space and demonstrate that color as a single cue has a relatively good performance in identifying persons under greatly varying imaging conditions.”
• Person Re-Identification by Camera Correlation Aware Feature Augmentation (2017, methodology): “In current person re-id literature, the best performers are discriminative learning basedmethods [2], [3], [4], [5], [6], [7],”
• 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].”
• Deep Metric Learning for Person Re-identification (2014, background): “The recent advances in this aspect are color invariant signature [10] and salience matching [11].”