  Color image segmentation is a crucial step in many computer vision and
pattern recognition applications. This article introduces an adaptive and
unsupervised clustering approach based on Voronoi regions, which can be applied
to solve the color image segmentation problem. The proposed method performs
region splitting and merging within Voronoi regions of the Dirichlet
Tessellated image (also called a Voronoi diagram) , which improves the
efficiency and the accuracy of the number of clusters and cluster centroids
estimation process. Furthermore, the proposed method uses cluster centroid
proximity to merge proximal clusters in order to find the final number of
clusters and cluster centroids. In contrast to the existing adaptive
unsupervised cluster-based image segmentation algorithms, the proposed method
uses K-means clustering algorithm in place of the Fuzzy C-means algorithm to
find the final segmented image. The proposed method was evaluated on three
different unsupervised image segmentation evaluation benchmarks and its results
were compared with two other adaptive unsupervised cluster-based image
segmentation algorithms. The experimental results reported in this article
confirm that the proposed method outperforms the existing algorithms in terms
of the quality of image segmentation results. Also, the proposed method results
in the lowest average execution time per image compared to the existing methods
reported in this article.
