Sparse Representation, Modeling and Learning in Visual Recognition

Theory, Algorithms and Applications

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Application Software, Computer Graphics, General Computing
Cover of the book Sparse Representation, Modeling and Learning in Visual Recognition by Hong Cheng, Springer London
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Author: Hong Cheng ISBN: 9781447167143
Publisher: Springer London Publication: May 25, 2015
Imprint: Springer Language: English
Author: Hong Cheng
ISBN: 9781447167143
Publisher: Springer London
Publication: May 25, 2015
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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