Texture Classification Through Combination of Sequential Colour Texture Classifiers

  • Francesco Bianconi
  • Antonio Fernández
  • Elena González
  • Fernando Ribas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

The sequential approach to colour texture classification relies on colour histogram clustering before extracting texture features from indexed images. The basic idea of such methods is to replace the colour triplet (RGB, HSV, Lab, etc.) associated to a pixel, by a scalar value, which represents an index of a colour palette. In this paper we studied different implementations of such approach. An experimental campaign was carried out over a database of 100 textures. The results show that the choice of a particular colour representation can improve classification performance with respect to grayscale conversion. We also found strong interaction effects between colour representation and feature space. In order to improve accuracy and robustness of classification, we have tested three well known expert fusion schemes: weighted vote, and a posteriori probability fusion (sum and product rules). The results demonstrate that combining different sequential approaches through classifier fusion is an effective strategy for colour texture classification.

Keywords

Classifier fusion Colour texture classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Francesco Bianconi
    • 1
  • Antonio Fernández
    • 2
  • Elena González
    • 2
  • Fernando Ribas
    • 3
  1. 1.Università degli Studi di Perugia, Dipartimento Ingegneria Industriale, Via G. Duranti 67 - 06125 PerugiaItalia
  2. 2.Universidade de Vigo, Departamento de Disen̈o en la Ingeniería, E.T.S.I.I. - Campus Universitario, 36310 Vigo - Espan̈a 
  3. 3.Universidade de Vigo, Departamento de Física Aplicada, E.U.I.T.I. - Torrecedeira 86, 36208 Vigo - Espan̈a 

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