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Robust Recognition via Information Theoretic Learning, Paperback / softback Book

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:
  • Category: Computer vision
  • ISBN: 9783319074153

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