Automatic selection of multiple texture feature extraction methods for texture pattern classification

Authors:
Domènec Puig;Miguel Ángel Garcia
Affiliations:
Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University, Tarragona, Spain;Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University, Tarragona, Spain
Venue:
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

Citing 5
Cited 0

Unsupervised Texture Segmentation in a Deterministic Annealing Framework

IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study

IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Similarity Measure Using Kullback-Leibler Divergence between Gamma Distributions

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Improving Texture Pattern Recognition by Integration of Multiple Texture Feature Extraction Methods

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Feature Selection Algorithms: A Survey and Experimental Evaluation

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining

Quantified Score

Hi-index	0.00

Visualization

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.