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For unsupervised color image segmentation, we propose a two-stage algorithm, KmsGC, that combines K-means clustering with graph cut. In the first stage, K-means clustering algorithm is applied to make an initial clustering, and the optimal number of clusters is automatically determined by a compactness criterion that is established to find clustering with maximum intercluster distance and minimum intracluster variance. In the second stage, a multiple terminal vertices weighted graph is constructed based on an energy function, and the image is segmented according to a minimum cost multiway cut. A large number of performance evaluations are carried out, and the experimental results indicate the proposed approach is effective compared to other existing image segmentation algorithms on the Berkeley image database.

KmsGC: An Unsupervised Color Image Segmentation Algorithm Based on K-Means Clustering and Graph Cut