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Robust Recognition Via Information Theoretic Learning

by Ran He
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Current price ₹3,672.00
Original price ₹5,649.00
Original price ₹5,649.00
Original price ₹5,649.00
(-35%)
₹3,672.00
Current price ₹3,672.00

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Book cover type: Paperback
  • ISBN13: 9783319074153
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 110
  • Original Price: EUR 49.99
  • Language: English
  • Edition: 2014
  • Item Weight: 199 grams
  • BISAC Subject(s): Artificial Intelligence / Computer Vision & Pattern Recognition, Software Development & Engineering / Computer Graphics, and Image Processing

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.

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