Generalized competitive clustering for image segmentation

In this paper, we focus on the problem of unsupervised clustering which allows automatic setting of optimal clusters number. We present a generalization of the competitive agglomeration clustering algorithm firstly introduced in [1]. This generalization is inspired by the regularization theory and suggests a new schema for using various cluster validity criteria continuously proposed in the literature. As a consequence of this generalization, we introduce new objective clustering functions, and present their associated optimal solutions. We present an application of this competitive clustering schema to color image segmentation in order to perform partial queries in the context of image retrieval by content. In this case, each pixel is represented by the color distribution in its vicinity. Clustering algorithm has to incorporate an appropriate distance measure to compare feature vectors similarity.