Dominant Set Based Data Clustering and Image Segmentation

Abstract

Clustering is an important approach in image segmentation. While various clustering algorithms have been proposed, the majority of them require one or more parameters as input, making them a little inflexible in practical applications. In order to solve the parameter dependent problem, in this paper we present a parameter-free clustering algorithm based on the dominant sets. We firstly study the influence of regularization parameters on the dominant sets clustering results. As a result, we select an appropriate regularization parameter to generate over-segmentation in clustering results. In the next step we merge clusters based on the relationship between intra-cluster and inter-cluster similarities. While being simple, our algorithm is shown to improve the clustering quality significantly in comparison with the dominant sets algorithm in data clustering and image segmentation experiments. It also performs comparably to or better than some other clustering algorithms with manually selected parameters input.