Image segmentation by unsupervised sparse clustering
Pattern Recognition Letters
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In this paper, we present a novel solution of image segmentation based on positiveness by regarding the segmentation as one of the graph-theoretic clustering problems. On the contrary to spectral clustering methods using eigenvectors, the proposed method tries to find an additive combination of positive components from an originally positive data-driven matrix. By using the positiveness constraint, we obtain sparsely clustered results which are closelyrelated to human perception and thus we call this method sparse clustering. The proposed method adopts a binary tree structure and solves a model selection problem by automatically determining the number of clusters using intra- and inter-cluster measures. We tested our method with various kinds of data such as points, gray-scale, color, and texture images. Experimental results show that the proposed method provides very successful and encouraging segmentations.