Texture classification through combination of sequential colour texture classifiers

Authors:
Francesco Bianconi;Antonio Fernández;Elena González;Fernando Ribas
Affiliations:
Università degli Studi di Perugia, Dipartimento Ingegneria Industriale, Perugia, Italia;Universidade de Vigo, Departamento de Diseño en la Ingeniería, E.T.S.I.I. Vigo, España;Universidade de Vigo, Departamento de Diseño en la Ingeniería, E.T.S.I.I. Vigo, España;Universidade de Vigo, Departamento de Física Aplicada, E.U.I.T.I., Vigo, España
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

Citing 11
Cited 2

Color quantization by dynamic programming and principal analysis

ACM Transactions on Graphics (TOG)
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in colour texture analysis

Pattern Recognition Letters
A framework for texture classification using the coordinated clusters representation

Pattern Recognition Letters
Classifier Conditional Posterior Probabilities

SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Handbook Of Pattern Recognition And Computer Vision

Handbook Of Pattern Recognition And Computer Vision
Pattern Recognition, Third Edition

Pattern Recognition, Third Edition
Evaluation of the effects of Gabor filter parameters on texture classification

Pattern Recognition

Texture Description Through Histograms of Equivalent Patterns

Journal of Mathematical Imaging and Vision
A sequential machine vision procedure for assessing paper impurities

Computers in Industry

Quantified Score

Hi-index	0.00

Visualization

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.