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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.
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
authorsdiscuss 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."
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