Paper Info
Title
Automatic selection of multiple texture feature extraction methods for texture pattern classification
Abstract
Texture-based pixel classification has been traditionally carried out by applying texture feature extraction methods that belong to a same family (e.g., Gabor filters). However, recent work has shown that such classification tasks can be significantly improved if multiple texture methods from different families are properly integrated. In this line, this paper proposes a new selection scheme that automatically determines a subset of those methods whose integration produces classification results similar to those obtained by integrating all the available methods but at a lower computational cost. Experiments with real complex images show that the proposed selection scheme achieves better results than well-known feature selection algorithms, and that the final classifier outperforms recognized texture classifiers.
Year
DOI
Venue
2005
10.1007/11492542_27
IbPRIA (2)
Keywords
Field
DocType
gabor filter,proposed selection scheme,multiple texture feature extraction,classification task,texture pattern classification,automatic selection,well-known feature selection algorithm,multiple texture method,texture-based pixel classification,texture classifier,classification result,texture feature extraction method,new selection scheme,feature selection
Computer vision,Texture compression,Feature selection,Pattern recognition,Computer science,Pixel classification,Image processing,Feature extraction,Gabor filter,Artificial intelligence,Classifier (linguistics),Texture filtering
Conference
Volume
ISSN
ISBN
3523
0302-9743
3-540-26154-0
Citations 
PageRank 
References 
0
0.34
11
Authors
2
Name
Order
Citations
PageRank
Domènec Puig1847.98
Miguel Ángel Garcia222024.41