Cheng Sparse Representation, Modeling and Learning in Visual Recognition

Theory, Algorithms and Applications
2015
ISBN: 978-1-4471-6714-3
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 257 Seiten, eBook

Reihe: Advances in Computer Vision and Pattern Recognition

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.

Zielgruppe

Research

Autoren/Hrsg.

Cheng, Hong

Inhaltsverzeichnis

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

Über Autor(innen)

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.