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Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications.

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 vi...

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Bibliographic Details
Authors and Corporations: Cheng, Hong.
Type of Resource: E-Book
Language: English
published:
London : Springer London, Limited, 2015.
©2015.
Series: Advances in Computer Vision and Pattern Recognition Ser.
Subjects:
Source: Ebook Central
ISBN: 9781447167143
Description
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.
Physical Description: 1 online resource (259 pages)
ISBN: 9781447167143