Fu Y. (Ed.) Low-Rank and Sparse Modeling for Visual Analysis

Springer, 2014. — 240 p.
This book provides a unique view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding, and learning among unconstrained visual data. These techniques will significantly advance existing methodologies of image and video analysis and understanding by taking advantage of low-rank and sparse modeling. Visual analysis under uncertainty stands at the core of numerous real-world applications which bring broad impacts and generate significant academic and industrial values. As a professional reference book and research monograph, this book, through several chapters covers popular research topics from several fields such as Pattern Recognition, Computer Vision, Big Data, Social Media, Image and Video Classification, and Machine Learning. These chapters, contributed by the editor, top experts, and practitioners, complement each other from various perspective and compose a solid overview of the lowrank and sparse modelling techniques for visual analysis. Readers from different backgrounds may all benefit from the well-balanced contents for both theoretical analysis and real-world applications.