Robust Recognition via Information Theoretic Learning Paperback / softback
Part of the SpringerBriefs in Computer Science series
This Springer Brief represents a comprehensive review of information theoretic methods for robust recognition.
A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy. The authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems.
For computational efficiency, the brief introduces the additive and multiplicative forms of half-quadratic optimization to efficiently minimize entropy problems and a two-stage sparse presentation framework for large scale recognition problems.
It also describes the strengths and deficiencies of different robust measures in solving robust recognition problems.
- Format: Paperback / softback
- Pages: 110 pages, 12 Tables, black and white; 25 Illustrations, color; 4 Illustrations, black and white; XI
- Publisher: Springer International Publishing AG
- Publication Date: 29/08/2014
- Category: Computer vision
- ISBN: 9783319074153
- PDF from £42.49