DSET++: A robust clustering algorithm
Jian Hou, Xu E, Lei Chi, Qi Xia, Naiming Qi · 2013
Clustering image pixels is an important technique in image segmentation. While normalized cuts is popularly used in image segmentation, dominant set clustering is another promising method and shown to outperform normalized cuts in some experiments. However, dominant set clustering suffers from the problems of sensitiveness to distance measures and over-segmentation tendency. In this paper we present DSET++ to enhance the original dominant set clustering and solve the two problems. Firstly, we use the histogram equalization in image enhancement to transform the similarity matrix and eliminate the sensitiveness to distance measures. In the second step we extend the dominant set based on density information to overcome the tendency of over-segmentation. Preliminary experiments on data clustering tasks validate the effectiveness of DSET++ clustering.