Automatic texture feature selection for image pixel classification

Authors:
Domenec Puig;Miguel Angel Garcia
Affiliations:
Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University, Avda. Paisos Catalans 26, 43007 Tarragona, Spain;Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University, Avda. Paisos Catalans 26, 43007 Tarragona, Spain
Venue:
Pattern Recognition
Year:
2006

Citing 4
Cited 9

Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners

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

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

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection Algorithms: A Survey and Experimental Evaluation

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

On Combining Classifiers by Relaxation for Natural Textures in Images

HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
On combining classifiers through a fuzzy multicriteria decision making approach: Applied to natural textured images

Expert Systems with Applications: An International Journal
Intelligent visual recognition and classification of cork tiles with neural networks

IEEE Transactions on Neural Networks
A Hopfield Neural Network for combining classifiers applied to textured images

Neural Networks
Comparative evaluation of classical methods, optimized gabor filters and LBP for texture feature selection and classification

CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Application-independent feature selection for texture classification

Pattern Recognition
Identifying user preferences with Wrapper-based Decision Trees

Expert Systems with Applications: An International Journal
Unsupervised texture-based image segmentation through pattern discovery

Computer Vision and Image Understanding
Fuzzy-rough feature selection aided support vector machines for Mars image classification

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.01

Visualization

Abstract

Pixel-based texture classifiers and segmenters are typically based on the combination of texture feature extraction methods that belong to a single family (e.g., Gabor filters). However, combining texture methods from different families has proven to produce better classification results both quantitatively and qualitatively. Given a set of multiple texture feature extraction methods from different families, this paper presents a new texture feature selection scheme that automatically determines a reduced subset of methods whose integration produces classification results comparable to those obtained when all the available methods are integrated, but with a significantly lower computational cost. Experiments with both Brodatz and real outdoor images show that the proposed selection scheme is more advantageous than well-known general purpose feature selection algorithms applied to the same problem.