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Sparse Representation, Modeling and Learning in Visual 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: 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.
Product Details

Table of Contents

Part I: Introduction and FundamentalsIntroductionThe Fundamentals of Compressed SensingPart II: Sparse Representation, Modeling and LearningSparse Recovery ApproachesRobust Sparse Representation, Modeling and LearningEfficient Sparse Representation and ModelingPart III: Visual Recognition ApplicationsFeature Representation and LearningSparsity Induced SimilaritySparse Representation and Learning Based ClassifiersPart IV: Advanced TopicsBeyond SparsityAppendix A: MathematicsAppendix B: Computer Programming Resources for Sparse Recovery ApproachesAppendix C: The source Code of Sparsity Induced SimilarityAppendix D: Derivations

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.

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