An optimum feature extraction method for texture classification

Authors:
Engin Avci;Abdulkadir Sengur;Davut Hanbay
Affiliations:
Firat University, Department of Electronic and Computer Science, 23119, Elazig, Turkey;Firat University, Department of Electronic and Computer Science, 23119, Elazig, Turkey;Firat University, Department of Electronic and Computer Science, 23119, Elazig, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 9
Cited 2

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Texture classification using multiresolution Markov random field models

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Filtering methods for texture discrimination

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Texture classification using wavelet transform

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Information-theoretic wavelet packet subband selection for texture classification

Signal Processing
Texture classification with combined rotation and scale invariant wavelet features

Pattern Recognition
Comparison and fusion of multiresolution features for texture classification

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Entropy-based algorithms for best basis selection

IEEE Transactions on Information Theory - Part 2
On the selection of an optimal wavelet basis for texture characterization

IEEE Transactions on Image Processing

An improved genetic algorithm for optimal feature subset selection from multi-character feature set

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Optimizing the operating conditions in a high precision industrial process using soft computing techniques

Expert Systems: The Journal of Knowledge Engineering

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Abstract

Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. In this paper a novel method, which is an intelligent system for texture classification is introduced. It used a combination of genetic algorithm, discrete wavelet transform and neural network for optimum feature extraction from texture images. An algorithm called the intelligent system, which processes the pattern recognition approximation, is developed. We tested the proposed method with several texture images. The overall success rate is about 95%.