A three-level clustering algorithm for color texture segmentation

Authors:
M. Sujaritha;S. Annadurai
Affiliations:
J.J. College of Engineering and Technology, Trichy;Directorate of Technical Education (PolyTech), Guindy, Chennai
Venue:
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Year:
2010

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Abstract

This paper presents the development of a three level unsupervised segmentation framework based on color and texture features. An important contribution of this work consists of a new formulation of three different clustering algorithms at three different levels. In the first level, a multiclass clustering algorithm using binary quaternion moment preserving thresholding algorithm is applied in order to quantize the colors. In the second level, clustering is performed on the quantized image using Self-organizing map for the estimation of the optimal number of components in the image and to resolve the initialization problem of mixture model based clustering which is carried out in the third level. The clusters obtained in the second level are then, refined and modelled using an adaptive spatial finite mixture model in the color-texture feature space. Since the dimensionality and the complexity of the image space is reduced at every level the proposed algorithm is fast and efficient. The proposed algorithm is applied on the Berkeley database images and complex natural images. The results are competent with the JSEG and CTex algorithms.