Robust Recognition via Information Theoretic Learning

von Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang

110 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

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.

• Reihe: SpringerBriefs in Computer Science
• ISBN: 9783319074153
• Sprache: Englisch
• Erscheinungsdatum: 09.09.2014
• Genre: Informatik, EDV/Anwendungs-Software
• Verlag: Springer International Publishing