EAN: 9783319074153

SPRINGERBRIEFS IN COMPUTER SCIENCE ROBUST REC...

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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