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Artificial neural network-based texture classification using reduced multidirectional Gabor features


Citation

Ashour, Mohammed W. and Khalid, Fatimah and Abdullah, Lili Nurliyana and Abdul Halin, Alfian (2014) Artificial neural network-based texture classification using reduced multidirectional Gabor features. International Review on Computers and Software, 9 (6). pp. 1007-1016. ISSN 1828-6003; ESSN: 1828-6011

Abstract / Synopsis

In this paper, a technique to classify Engineering Machined Textures (EMT) into the six classes of Turning, Grinding, Horizontal-Milling, Vertical-Milling, Lapping and Shaping, is presented. Multidirectional Gabor features are firstly extracted from each image followed by a dimensionality reduction step using Principal Components Analysis (PCA). The images are finally classified using a supervised Artificial Neural Network (ANN) classifier. Experimental results using a 72-image dataset demonstrate that PCA is able to reduce computational time while improving classification accuracy. In addition, the use of the proposed Gabor filter seems to be more robust compared to other existing techniques.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
Publisher: Praise Worthy Prize
Keywords: Texture features extraction; Features reduction; ANN classification; Gabor filter; Principal component analysis
Depositing User: Nabilah Mustapa
Date Deposited: 25 Jun 2015 14:02
Last Modified: 24 Aug 2015 10:53
URI: http://psasir.upm.edu.my/id/eprint/36537
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