ENGLISH

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

By Hong Cheng (auth.)

Book information

Publisher
Springer-Verlag London
Year
2015
ISBN
978-1-4471-6713-6, 978-1-4471-6714-3
DOI
10.1007/978-1-4471-6714-3
Language
english
Format
PDF
Filesize
8 MB (8404403 bytes)
Series
Advances in Computer Vision and Pattern Recognition
Edition
1
Pages
257\259
Time added
2015-07-18 18:00:00
Pattern Recognition; Image Processing and Computer

Description

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.

Similar books