Voronoï region-based adaptive unsupervised color image segmentation. (May 2017) - Hettiarachchi, R.; Peters, J.F.
Abstract: Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This paper introduces an adaptive and unsupervised approach based on Voronoï regions to solve the color image segmentation problem. The proposed method uses a hybrid of spatial and feature space Dirichlet tessellation followed by inter-Voronoï region proximal cluster merging to automatically find the number of clusters and cluster centroids in an image. Since, the Voronoï regions are much smaller compared to the whole image, Voronoï region-wise clustering improves the efficiency and accuracy of the number of clusters and cluster centroid estimation process. The proposed method was compared with four other adaptive unsupervised cluster-based image segmentation algorithms on three image segmentation evaluation benchmarks. The experimental results reported in this paper confirm that the proposed method outperforms the existing algorithms in terms of the image segmentation quality and results in much lower average execution time per image. Abstract : Highlights: We propose a hybrid of Dirichlet tessellation to automatically segment an image. Spatial Dirichlet tessellation adaptively divides an image into Voronoï regions. Feature space Dirichlet tessellation adaptively clusters pixels in Voronoï regions. Inter-Voronoi region proximal cluster merging automatically finds the final clusters.