Superpixel Based Hierarchical Segmentation for Color Image

Chong WU  Le ZHANG  Houwang ZHANG  Hong YAN  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.10   pp.2246-2249
Publication Date: 2020/10/01
Publicized: 2020/07/03
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDL8025
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
image segmentation,  robustness,  superpixel,  

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In this letter, we propose a hierarchical segmentation (HS) method for color images, which can not only maintain the segmentation accuracy, but also ensure a good speed. In our method, HS adopts the fuzzy simple linear iterative clustering (Fuzzy SLIC) to obtain an over-segmentation result. Then, HS uses the fast fuzzy C-means clustering (FFCM) to produce the rough segmentation result based on superpixels. Finally, HS takes the non-iterative K-means clustering using priority queue (KPQ) to refine the segmentation result. In the validation experiments, we tested our method and compared it with state-of-the-art image segmentation methods on the Berkeley (BSD500) benchmark under different types of noise. The experiment results show that our method outperforms state-of-the-art techniques in terms of accuracy, speed and robustness.