Sparse Representation, Modeling and Learning in Visual Recognition

Hong Cheng

ISBN 10: 1447167139 / ISBN 13: 9781447167136

Published by Springer, Springer Jun 2015, 2015

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 272 pp. Englisch.

Seller Inventory # 9781447167136

Bibliographic Details

• Title: Sparse Representation, Modeling and Learning in Visual Recognition
• Publisher: Springer, Springer Jun 2015
• Publication Date: 2015
• Language: English
• ISBN 10: 1447167139
• ISBN 13: 9781447167136
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 21D millimeters 241 millimeters width by 160 millimeters height by 21 millimeters depth
• Weight: 576 grams

About this title

Synopsis

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.

About the Author

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.