Application-independent feature selection for texture classification

Authors:
Domenec Puig;Miguel Angel Garcia;Jaime Melendez
Affiliations:
Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University, Av. Paisos Catalans 26, 43007 Tarragona, Spain;Department of Informatics Engineering, Autonomous University of Madrid, Francisco Tomas y Valiente 11, 28049 Madrid, Spain;Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University, Av. Paisos Catalans 26, 43007 Tarragona, Spain
Venue:
Pattern Recognition
Year:
2010

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Abstract

Recent developments in texture classification have shown that the proper integration of texture methods from different families leads to significant improvements in terms of classification rate compared to the use of a single family of texture methods. In order to reduce the computational burden of that integration process, a selection stage is necessary. In general, a large number of feature selection techniques have been proposed. However, a specific texture feature selection must be typically applied given a particular set of texture patterns to be classified. This paper describes a new texture feature selection algorithm that is independent of specific classification problems/applications and thus must only be run once given a set of available texture methods. The proposed application-independent selection scheme has been evaluated and compared to previous proposals on both Brodatz compositions and complex real images.