Sparse Representation, Modeling and Learning in Visual Recognition Theory, Algorithms and Applications
Synopsis
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.Book details
- Edition:
- 2015
- Series:
- Advances in Computer Vision and Pattern Recognition
- Author:
- Hong Cheng
- ISBN:
- 9781447167143
- Related ISBNs:
- 9781447167136
- Publisher:
- Springer London
- Pages:
- N/A
- Reading age:
- Not specified
- Includes images:
- Yes
- Date of addition:
- 2019-08-11
- Usage restrictions:
- Copyright
- Copyright date:
- 2015
- Copyright by:
- Springer London, London
- Adult content:
- No
- Language:
- English
- Categories:
- Art and Architecture, Computers and Internet, Nonfiction