Hierarchical Image Segmentation Based on Iterative Contraction and Merging
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 contracti...
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| Veröffentlicht in: | IEEE transactions on image processing 2017-05, Vol.26 (5), p.2246-2260 |
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| container_title | IEEE transactions on image processing |
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| creator | Syu, Jia-Hao Wang, Sheng-Jyh Wang, Li-Chun |
| description | 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. |
| doi_str_mv | 10.1109/TIP.2017.2651395 |
| format | Article |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Affinity matrix Algorithm design and analysis Clustering algorithms contraction process hierarchical image segmentation Image color analysis Image resolution Image segmentation Merging Optimization |
| title | Hierarchical Image Segmentation Based on Iterative Contraction and Merging |
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