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Classification of ordered texture images using regression modelling and granulometric features

Khatun, Mahmuda and Gray, Alison and Marshall, Stephen (2011) Classification of ordered texture images using regression modelling and granulometric features. In: Irish Machine Vision and Image Processing Conference, 2011-09-08 - 2011-09-09.

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Abstract

Structural information available from the granulometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which follow an intrinsic ordering of textures, using polynomial regression to express granulometric moments as a function of class label. Separate models are built for each individual moment and combined for back-prediction of the class label of a new image. The methodology was developed on synthetic images of evolving textures and tested using real images of 8 different grades of cut-tear-curl black tea leaves. For comparison, grey level co-occurrence (GLCM) based features were also computed, and both feature types were used in a range of classifiers including the regression approach. Experimental results demonstrate the superiority of the granulometric moments over GLCM-based features for classifying these tea images.

Item type: Conference or Workshop Item (Paper)
ID code: 33186
Keywords: granulometry, structuring, pattern spectrum, Mathematics, Probabilities. Mathematical statistics, Electrical engineering. Electronics Nuclear engineering
Subjects: Science > Mathematics
Science > Mathematics > Probabilities. Mathematical statistics
Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Science > Mathematics and Statistics
Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
Depositing user: Pure Administrator
Date deposited: 13 Sep 2011 14:11
Last modified: 21 Sep 2017 15:47
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URI: https://strathprints.strath.ac.uk/id/eprint/33186
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