Fast Minimum-Spanning-Tree-like Image Segmentation
Byungki Cha, Hideaki Kawano, Noriaki Suetake, Takashi Aso · 2008
We propose a fast segmentation technique for segmenting natural images into meaningful regions based on a minimum spanning tree (MST) principle. The main idea of the technique comes from the redundancy of MST based image segmentation process. We compared the performance of the proposed technique with the performance of depth first search(DFS) and of the traditional MST. Finally, the simple split and merge method based on the proposed technique is proposed with some natural images of the Berkeley segmentation dataset and compared with our results with human’s handi-labeled segmentation results.