Cross-View Projective Dictionary Learning for Person Re-Identification
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Summary
This work proposes a cross-view projective dictionary learning (CPDL) approach, which learns effective features for persons across different views through dictionary learning, and designs two objectives to learn low-dimensional representations for each pedestrian in the patch-level and the image-level.
- Type
- article
- Published
- 2015-07-25
- Cited by
- 100
- References
- 31
- OpenAlex
- https://openalex.org/W1142012885
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17676461
Keywords
Discriminative model, Computer science, Artificial intelligence, Representation (politics), Feature learning
References
- Evaluating Appearance Models for Recognition, Reacquisition, and Tracking
- Person Re-identification by Attributes
- Person re-identification by symmetry-driven accumulation of local features
- DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
- Person re-identification by probabilistic relative distance comparison
- Transfer re-identification: From person to set-based verification
- Semi-supervised Coupled Dictionary Learning for Person Re-identification
- Learning low-rank and discriminative dictionary for image classification
- Discriminative K-SVD for dictionary learning in face recognition
- Unsupervised Salience Learning for Person Re-identification
- PCCA: A new approach for distance learning from sparse pairwise constraints
- BiCov: a novel image representation for person re-identification and face verification
- Large scale metric learning from equivalence constraints
- Learning Mid-level Filters for Person Re-identification
- Distance Metric Learning for Large Margin Nearest Neighbor Classification
- Local Fisher Discriminant Analysis for Pedestrian Re-identification
- Person Re-identification by Salience Matching
- Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation
- rm K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
- Projective dictionary pair learning for pattern classification
Cited by
- Person Re-Identification by Saliency Learning
- Multi-Region bilinear convolutional neural networks for person re-identification
- Large Scale Similarity Learning Using Similar Pairs for Person Verification
- Metric Embedded Discriminative Vocabulary Learning for High-Level Person Representation
- Learning a Multi-class Discriminative Dictionary with Nonredundancy Constraints for Visual Classification
- Multi-View Time Series Classification: A Discriminative Bilinear Projection Approach
- Person Reidentification Using Deep Convnets With Multitask Learning
- Deep Neural Networks with Inexact Matching for Person Re-Identification
- Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning
- Spatial Pyramid-Based Statistical Features for Person Re-Identification: A Comprehensive Evaluation
- Scale-Adaptive Low-Resolution Person Re-Identification via Learning a Discriminating Surface
- Video-Based Person Re-Identification by Simultaneously Learning Intra-Video and Inter-Video Distance Metrics
- Learning Robust Representations for Data Analytics
- Empirical Risk Minimization for Metric Learning Using Privileged Information
- Late Fusion in Part-based Person Re-identification
- Constraint patch matching for faster person re-identification
- Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image
- Part-Based Deep Hashing for Large-Scale Person Re-Identification
- Cross-view semantic projection learning for person re-identification
- Enhancing Person Re-identification in a Self-Trained Subspace
Related papers
- Large scale metric learning from equivalence constraints
- An improved deep learning architecture for person re-identification
- DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
- Person re-identification by symmetry-driven accumulation of local features
- Scalable Person Re-identification: A Benchmark
- Learning Mid-level Filters for Person Re-identification
- Unsupervised Salience Learning for Person Re-identification
- Semi-supervised Coupled Dictionary Learning for Person Re-identification