Image Segmentation using SLIC Superpixels and Affinity Propagation Clustering

In this paper, we propose a new method of image segmentation, named SLICAP, which combines the simple linear iterative clustering (SLIC) method with the affinity propagation (AP) clustering algorithm. First, the SLICAP technique uses the SLIC superpixel algorithm to form an over-segmentation of an image. Then, a similarity is constructed based on the features of superpixels. Finally, the AP algorithm clusters these superpixels with the similarities obtained. We compose three similarities attempt to find the most suitable one for SLICAP. Compared with the standard Ncuts method for image segmentation, the unsupervised SLICAP approach is relatively simple and fast, and there is no need to determine the number of targets. The experiments on the Berkeley segmentation database show that the image segmentation results produced by the SLICAP method are well consistent with the human visual perception. Quantitively, the SLICAP method outperforms other classical segmentation algorithms with the boundary-based and region-based criteria, including F-measure, probabilistic rand index, variation of information and boundary displacement error.