Robust Recognition via Information Theoretic Learning

He, Ran (Author)/ Hu, Baogang (Author)/ Yuan, Xiaotong (Author)/ Wang, Liang (Author)

ISBN 10: 3319074156 / ISBN 13: 9783319074153

Published by Springer, 2014

About this Item

2014 edition. 124 pages. 9.00x6.00x0.25 inches. In Stock.

Seller Inventory # 3319074156

Bibliographic Details

• Title: Robust Recognition via Information Theoretic Learning
• Publisher: Springer
• Publication Date: 2014
• Language: English
• ISBN 10: 3319074156
• ISBN 13: 9783319074153
• Binding: Paperback
• Condition: Brand New
• Weight: 0.2 kilograms

About this title

Synopsis

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.