Sparse Representation, Modeling and Learning in Visual Recognition Theory, Algorithms and Applications
This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in vi...
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| Format: | eBook |
| Language: | English |
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London
Springer London
2015, 2015
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| Edition: | 1st ed. 2015 |
| Series: | Advances in Computer Vision and Pattern Recognition
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Part I: Introduction and Fundamentals
- Introduction
- The Fundamentals of Compressed Sensing
- Part II: Sparse Representation, Modeling and Learning
- Sparse Recovery Approaches
- Robust Sparse Representation, Modeling and Learning
- Efficient Sparse Representation and Modeling
- Part III: Visual Recognition Applications
- Feature Representation and Learning
- Sparsity Induced Similarity
- Sparse Representation and Learning Based Classifiers
- Part IV: Advanced Topics
- Beyond Sparsity
- Appendix A: Mathematics
- Appendix B: Computer Programming Resources for Sparse Recovery Approaches
- Appendix C: The source Code of Sparsity Induced Similarity
- Appendix D: Derivations