Fast segmentation based on a hybrid of clustering and morphological approaches

This paper proposes a fast segmentation method for still image based on a hybrid of clustering and morphological segmentation approach. The objective of the clustering is to partition an input image into a number of clusters such that the gray levels within each cluster are similar. The clustered image is further processed by using morphological segmentation approach, in which a seeded region growing however plays a role of the decision tool instead of a watershed algorithm for a remarkable improvement of processing time. The performance of the proposed method is evaluated by comparing its region-based coding results with those of the morphological watershed-based segmentation method and the split-and-merge algorithm. The experiments results showed that region-based coding using the proposed algorithm yields PSNR improvement of about 1.5 dB over the morphological watershed-based method. Especially, the total time elapsed to segment an image using the proposed method is reduced about 1/6 and 1/3 compared with those of the watershed-based segmentation and the split-and-merge methods, respectively.