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Title
Sparse representation, modeling and learning in visual recognition [electronic resource] : theory, algorithms and applications / Hong Cheng.
ISBN
9781447167143 electronic book
1447167147 electronic book
9781447167136
Published
London : Springer, [2015]
Copyright
©2015
Language
English
Description
1 online resource.
Other Standard Identifiers
10.1007/978-1-4471-6714-3 doi
Call Number
TA1634 .C44 2015eb
Dewey Decimal Classification
006.4/2
Summary
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: Provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition 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 Researchers and graduate students interested in computer vision, pattern recognition and robotics will find this work to be an invaluable introduction to techniques of sparse representations and compressive sensing. Dr. Hong Cheng is Professor in the School of Automation Engineering, and Deputy Executive Director of the Center for Robotics at the University of Electronic Science and Technology of China. His other publications include the Springer book Autonomous Intelligent Vehicles.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (viewed May 28, 2015).
Series
Advances in computer vision and pattern recognition.
Available in Other Form
Print version: 9781447167136
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.
