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Proceedings Paper

Data mining on nonhomogenous textures
Author(s): Leena Lepisto; Iivari Kunttu; Jorma Autio; Ari J.E. Visa
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Paper Abstract

Clustering of the texture images is a demanding part of multimedia database mining. Most of the natural textures are non-homogenous in terms of color and textural properties. In many cases, there is a need for a system that is able to divide the non-homogenous texture images into visually similar clusters. In this paper, we introduce a new method for this purpose. In our clustering technique, the texture images are ordered into a queue based on their visual similarity. Based on this queue, similar texture images can be selected. In similarity evaluation, we use feature distributions that are based on the color and texture properties of the sample images. Color correlogram is a distribution that has proved to be effective in characterization of color and texture properties of the non-homogenous texture images. Correlogram is based on the co-occurrence matrix, which is a statistical tool in texture analysis. In this work, we use gray level and hue correlograms in the characterization of the colored texture. The similarity between the distributions is measured using several different distance measures. The queue of texture images is formed based on the distances between the samples. In this paper, we use a test set which contains non-homogenous texture images of ornamental stones.

Paper Details

Date Published: 21 March 2003
PDF: 9 pages
Proc. SPIE 5098, Data Mining and Knowledge Discovery: Theory, Tools, and Technology V, (21 March 2003); doi: 10.1117/12.486011
Show Author Affiliations
Leena Lepisto, Tampere Univ. of Technology (Finland)
Iivari Kunttu, Tampere Univ. of Technology (Finland)
Jorma Autio, Saanio & Riekkola Consulting Engineers (Finland)
Ari J.E. Visa, Tampere Univ. of Technology (Finland)

Published in SPIE Proceedings Vol. 5098:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology V
Belur V. Dasarathy, Editor(s)

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