Article

Journal of Mathematical Imaging and Vision

, Volume 47, Issue 1, pp 79-92

Open Access This content is freely available online to anyone, anywhere at any time.

Adaptive Matrices and Filters for Color Texture Classification

  • Ioannis GiotisAffiliated withJohann Bernoulli Institute for Mathematics and Computer Science, University of Groningen Email author 
  • , Kerstin BunteAffiliated withCITEC Center of Excellence—Cognitive Interaction Technology, Bielefeld University
  • , Nicolai PetkovAffiliated withJohann Bernoulli Institute for Mathematics and Computer Science, University of Groningen
  • , Michael BiehlAffiliated withJohann Bernoulli Institute for Mathematics and Computer Science, University of Groningen

Abstract

In this paper we introduce an integrative approach towards color texture classification and recognition using a supervised learning framework. Our approach is based on Generalized Learning Vector Quantization (GLVQ), extended by an adaptive distance measure, which is defined in the Fourier domain, and adaptive filter kernels based on Gabor filters. We evaluate the proposed technique on two sets of color texture images and compare results with those other methods achieve. The features and filter kernels learned by GLVQ improve classification accuracy and they are able to generalize much better for data previously unknown to the system.

Keywords

Adaptive metric Adaptive filters Classification Color texture analysis Gabor filters Learning Vector Quantization