title: Hierarchical Image Segmentation Based on Iterative Contraction and Merging.
abstract: In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging. In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference. After that, we iteratively perform region-based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation. Comparing with the state-of-the-art techniques, the proposed algorithm can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results.
authors.forename: Jia-Hao
authors.initials: JH
authors.lastname: Syu
authors.forename: Sheng-Jyh
authors.initials: SJ
authors.lastname: Wang
authors.forename: Li-Chun
authors.initials: LC
authors.lastname: Wang