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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications

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

Cheng, Hong

Paperback

Series: Advances in Computer Vision and Pattern Recognition

General ComputersProgramming

ISBN10: 1447172515
ISBN13: 9781447172512
Publisher: Springer Nature
Published: Oct 9 2016
Pages: 257
Weight: 0.85
Height: 0.57 Width: 6.14 Depth: 9.21
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.

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