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

✍ Scribed by Omar Oreifej, Mubarak Shah (auth.)

• Publisher: Springer International Publishing
• Year: 2014
• Tongue: English
• Leaves: 116
• Series: The International Series in Video Computing 12
• Edition: 1
• Category: Library

✦ 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.

✦ Table of Contents

Front Matter....Pages i-vi
Introduction....Pages 1-7
Background and Literature Review....Pages 9-19
Seeing Through Water: Underwater Scene Reconstruction....Pages 21-36
Simultaneous Turbulence Mitigation and Moving Object Detection....Pages 37-54
Action Recognition by Motion Trajectory Decomposition....Pages 55-67
Complex Event Recognition Using Constrained Rank Optimization....Pages 69-93
Concluding Remarks....Pages 95-99
Extended Derivations for Chapter 4....Pages 101-108
Back Matter....Pages 109-114

✦ Subjects

Computer Imaging, Vision, Pattern Recognition and Graphics