Hierarchical Image Segmentation Based on Iterative Contraction and Merging

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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.

Original languageEnglish
Article number7814291
Pages (from-to)2246-2260
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number5
StatePublished - 1 May 2017


  • Affinity matrix
  • contraction process
  • hierarchical image segmentation


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