Adaptive matrices for color texture classification

Authors:
Kerstin Bunte;Ioannis Giotis;Nicolai Petkov;Michael Biehl
Affiliations:
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands;Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands;Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands;Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Year:
2011

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Abstract

In this paper we introduce an integrative approach towards color texture classification learned by a supervised framework. Our approach is based on theGeneralized LearningVectorQuantization (GLVQ), extended by an adaptive distance measure which is defined in the Fourier domain and 2D Gabor filters. We evaluate the proposed technique on a set of color texture images and compare results with those achieved by methods already existing in the literature. The features learned by GLVQ improve classification accuracy and they generalize much better for evaluation data previously unknown to the system.