Data mining on nonhomogenous textures

Leena Lepisto; Iivari Kunttu; Jorma Autio; Ari J.E. Visa

doi:10.1117/12.486011

21 March 2003 Data mining on non-homogenous textures

Leena Lepisto, Iivari Kunttu, Jorma Autio, Ari J.E. Visa

Proceedings Volume 5098, Data Mining and Knowledge Discovery: Theory, Tools, and Technology V; (2003) https://doi.org/10.1117/12.486011
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

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Leena Lepisto, Iivari Kunttu, Jorma Autio, and Ari J.E. Visa "Data mining on non-homogenous textures", Proc. SPIE 5098, Data Mining and Knowledge Discovery: Theory, Tools, and Technology V, (21 March 2003); https://doi.org/10.1117/12.486011

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