Robust Subspace Estimation Using Low-Rank Optimization : Theory and Applications

Oreifej, Omar; Shah, Mubarak

ISBN 10: 3319041835 / ISBN 13: 9783319041834

Published by Springer, 2014

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Seller Inventory # 21054948-n

Bibliographic Details

Title: Robust Subspace Estimation Using Low-Rank ...

Publisher: Springer

Publication Date: 2014

Binding: Hardcover

Condition: New

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Synopsis:

Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book, the authors discuss fundamental formulations and extensions for low-rank optimization-based subspace estimation and representation. By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate  how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition.