关键词: 轮廓盒子, 加权分水岭算法, 超度量轮廓图算法, 动态规划, 自适应匹配

Abstract: The shortages of conventional ultrametric contour map (UCM) algorithm are weak-adapted for contours, weak-adapted for hierarchies matching and lots of fragments for segmentation. To solve the shortages, this paper proposes a novel improved hierarchical image segmentation algorithm based on self-adapted objects and context matching strategy. First, the algorithm uses “edge boxes” to extract the key contours, and then uses the weighted watershed algorithm to combine contour regions, and the contours are transferred for UCM hierarchical trees. Then, a dynamic programming method is used for self-adapted objects and context matching. At last, the re-scaling UCM hierarchical trees are used for image segmentation. Experimental results on BSDS500 datasets show that the proposed algorithm has a significant improvement on image segmentation. Three measure indexes are segment cover (SC), probabilistic region index (PRI) and information variation (IV) which have achieved the best results on optimal dataset scale (ODS) and optimal image scale (OIS). The re-scaling UCM hierarchical trees satisfy the same hierarchies are segmented for the same objects, and reduce the fragments of image segmentation. The proposed algorithm is outperformed than the state-of-the-art algorithms for image segmentation, and satisfies the time comple-xity in a certain stage.

Key words: edge boxes, weighted watershed, ultrametric contour map, dynamic programming, self-adapted matching