Content-sensitive superpixel generation with boundary adjustment

Dong Zhang, Gang Xie, Jinchang Ren, Zhe Zhang, Wenliang Bao, Xinying Xu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
7 Downloads (Pure)


Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was used to measure the amount of information in the image, and the amount of information was evenly distributed to each seed. It placed more seeds to achieve the lower under-segmentation in content-dense regions, and placed the fewer seeds to increase computational efficiency in content-sparse regions. Second, the Prim algorithm was adopted to generate uniform superpixels efficiently. Third, a boundary adjustment strategy with the adaptive distance further optimized the superpixels to improve the performance of the superpixel. Experimental results on the Berkeley Segmentation Database show that our method outperforms competing methods under evaluation metrics.
Original languageEnglish
Article number3150
Number of pages16
JournalApplied Sciences
Issue number9
Publication statusPublished - 30 Apr 2020


  • contet-sensitive
  • superpixel
  • boundary adjustment


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