Superpixel Segmentation Based on Square-Wise Asymmetric Partition and Structural Approximation

Hua LI, Sam KWONG, Chuanbo CHEN, Yuheng JIA, Runmin CONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

15 Citations (Scopus)


Superpixel segmentation aims at grouping discretizing pixels into high-level correlative units and reducing the complexity of subsequent tasks, e.g., saliency detection and object tracking. Existing superpixel segmentation algorithms mainly focus on maintaining the geometrical information, while neglecting the irregular structure of superpixels. In this paper, a superpixel segmentation method is proposed to generate approximately structural superpixels with sharp boundary adherence and comprehensive semantic information. The superpixel segmentation is formulated as a square-wise asymmetric partition problem, where the semantic perceptual superpixels are recorded in a square level to preserve abundant semantic information and save storage simultaneously. Moreover, in order to achieve regular-shape superpixel units to better adhere to image boundaries and contours, a combinatorial optimization strategy is devised to achieve an optimal combination of squares and isolated pixels. Experimental comparisons with some state-of-the-art superpixel segmentation methods on the public benchmarks demonstrate the effectiveness of the proposed method quantitatively and qualitatively. In addition, we have applied the method to brain tissue segmentation to illustrate superior performance.
Original languageEnglish
Pages (from-to)2625-2637
JournalIEEE Transactions on Multimedia
Issue number10
Early online date25 Mar 2019
Publication statusPublished - Oct 2019
Externally publishedYes

Bibliographical note

This work was supported in part by the Natural Science Foundation of China under Grant 61672443, 61772344, and in part by the Hong Kong RGC General Research Funds under Grants 9042038 (CityU 11205314) and 9042322 (CityU 11200116).


  • combinatorial optimization
  • square-wise asymmetric partition
  • structural approximation
  • Superpixel


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