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To improve the segmentation quality and efficiency of color image, a novel approach which combines the advantages of the mean
shift (MS) segmentation and improved ant clustering method is proposed. The regions which can preserve the discontinuity characteristics
of an image are segmented by MS algorithm, and then they are represented by a graph in which every region is represented by
a node. In order to solve the graph partition problem, an improved ant clustering algorithm, called similarity carrying ant
model (SCAM-ant), is proposed, in which a new similarity calculation method is given. Using SCAM-ant, the maximum number of
items that each ant can carry will increase, the clustering time will be effectively reduced, and globally optimized clustering
can also be realized. Because the graph is not based on the pixels of original image but on the segmentation result of MS
algorithm, the computational complexity is greatly reduced. Experiments show that the proposed method can realize color image
segmentation efficiently, and compared with the conventional methods based on the image pixels, it improves the image segmentation
quality and the anti-interference ability. 相似文献
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