Paper Info
Title
Texture classification through combination of sequential colour texture classifiers
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.
Year
DOI
Venue
2007
10.1007/978-3-540-76725-1_25
CIARP
Keywords
Field
DocType
particular colour representation,expert fusion scheme,colour texture classification,texture classification,sequential colour texture classifier,classifier fusion,colour triplet,colour representation,colour palette,colour histogram,classification performance,interaction effect,indexation,feature space
Computer vision,Histogram,Feature vector,Pattern recognition,Computer science,Robustness (computer science),RGB color model,Artificial intelligence,Pixel,Cluster analysis,Grayscale,Texture filtering
Conference
Volume
ISSN
ISBN
4756
0302-9743
3-540-76724-X
Citations 
PageRank 
References 
5
0.44
13
Authors
4
Name
Order
Citations
PageRank
Francesco Bianconi131118.11
Antonio Fernández280149.47
Elena GonzáLez3502.61
Fernando Ribas450.44