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Construction of Co-occurrence Matrix using Gabor Wavelets for Classification of Arecanuts by Decision Trees

Suresha M, Ajit Danti Published in Pattern Recognition

International Journal of Applied Information Systems
Year of Publication: 2012
© 2012 by IJAIS Journal
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  1. Suresha M and Ajit Danti. Article: Construction of Co-occurrence Matrix using Gabor Wavelets for Classification of Arecanuts by Decision Trees. International Journal of Applied Information Systems 4(6):33-39, December 2012. BibTeX

    	author = "Suresha M and Ajit Danti",
    	title = "Article: Construction of Co-occurrence Matrix using Gabor Wavelets for Classification of Arecanuts by Decision Trees",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 4,
    	number = 6,
    	pages = "33-39",
    	month = "December",
    	note = "Published by Foundation of Computer Science, New York, USA"


In this paper, a novel method developed for classification of arecanuts based on texture features. A Gabor response co-occurrence matrix (GRCM) is constructed analogous to Gray Level co-occurrence matrix (GLCM). Classification is done using kNN and Decision Tree (DT) classifier based on GRCM features. There are twelve Gabor filters are designed (Four orientations and three scaling) to capture variations in lighting conditions. Results are compared with kNN classifier and found better results. Splitting rules for growing decision tree that are included are gini diversity index(gdi), twoing rule, and entropy. Proposed approach is experimented on large data set using cross validation and found good success rate in decision tree classifier.


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Arecanut, classification, Decision Trees, Gabor response co-occurrence matrix, Gabor Wavelets, Gray level co-occurrence matrix, Texture Features