Sparse representation, modeling and learning in visual recognition : theory, algorithms and applications
Author(s)
Bibliographic Information
Sparse representation, modeling and learning in visual recognition : theory, algorithms and applications
(Advances in computer vision and pattern recognition / Sameer Singh, Sing Bing Kang, series editors)
Springer, c2015
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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
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
by "Nielsen BookData"