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 visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.

Table of Contents

Part I Introduction and Fundamentals
Chapter 1 Introduction
Chapter 2 The Fundamentals of Compressed Sensing

Part II Sparse Representation, Modeling and Learning
Chapter 3 Sparse Recovery Approaches
Chapter 4 Robust Sparse Representation, Modeling and Learning
Chapter 5 Efficient Sparse Representation and Modeling

Part III Visual Recognition Applications
Chapter 6 Feature Representation and Learning
Chapter 7 Sparsity-Induced Similarity
Chapter 8 Sparse Representation and Learning-Based Classifiers

Part IV Advanced Topics
Chapter 9 Beyond Sparsity
Appendix A Mathematics
Appendix B Computer Programming Resourcesfor Sparse Recovery Approaches
Appendix C The Source Code of Sparsity InducedSimilarity