Content-sensitive superpixel generation with boundary adjustment

Zhang, Dong and Xie, Gang and Ren, Jinchang and Zhang, Zhe and Bao, Wenliang and Xu, Xinying (2020) Content-sensitive superpixel generation with boundary adjustment. Applied Sciences, 10 (9). 3150. ISSN 2076-3417

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    Abstract

    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.

    Creators(s): Zhang, Dong, Xie, Gang, Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194, Zhang, Zhe, Bao, Wenliang and Xu, Xinying;
    Item type:Article
    ID code:72422
    Keywords:contet-sensitive, superpixel, boundary adjustment, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Technology and Innovation Centre > Sensors and Asset Management
    Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:19 May 2020 10:30
    Last modified:29 May 2020 00:58
    URI:https://strathprints.strath.ac.uk/id/eprint/72422
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