Paper Info
Title
Pixel-Based Texture Classification By Integration Of Multiple Feature Extraction Methods Evaluated Over Multisized Windows
Abstract
This paper presents a pixel-based texture classifier oriented to the identification of texture models that can be present in an input image, given a set of models known in advance. The proposed methodology is based on the integration of texture features generated by texture methods that belong to different families, which are evaluated over multiple windows of different sizes. This is a novelty with respect to the current texture classifiers, which are based on specific families of texture methods evaluated over single windows of a size defined empirically. Experiments show that this integration strategy produces better results than classical texture classifiers based on specific families of texture methods.
Year
DOI
Venue
2007
10.1142/S0218001407005879
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
supervised texture classification, multiple texture methods, multiple evaluation windows, Kullback J-divergence, MeasTex
Computer vision,Texture compression,Pattern recognition,Image texture,Feature extraction,Artificial intelligence,Pixel,Novelty,Classifier (linguistics),Texture filtering,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
21
7
0218-0014
Citations 
PageRank 
References 
0
0.34
13
Authors
2
Name
Order
Citations
PageRank
Domènec Puig1847.98
Miguel Ángel Garcia222024.41