Semiconductor Optoelectronics, Volume. 43, Issue 3, 585(2022)

An Adaptive Initializing Superpixel Seed Points Method Based on Kmeans++

YANG Zhili and ZHANG Dong
Author Affiliations
  • [in Chinese]
  • show less

    As a preprocessing step of target segmentation, superpixel can greatly reduce the amount of subsequent data processing, and plays a vital role in image segmentation. In most superpixel algorithms, seed points are sampled on a regular grid or initialized randomly, which easily leads to undersegmentation. In order to obtain a good distribution of seed point and avoid undersegmentation, an adaptively initializing superpixel seeds method based on Kmeans++ is proposed and used to improve the algorithms of SNIC. The experimental results show that the improved SNIC algorithm can get higher boundary recall rate and lower undersegmentation error rate than that of the traditional algorithm without a lot of computational cost.

    Tools

    Get Citation

    Copy Citation Text

    YANG Zhili, ZHANG Dong. An Adaptive Initializing Superpixel Seed Points Method Based on Kmeans++[J]. Semiconductor Optoelectronics, 2022, 43(3): 585

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Jan. 13, 2022

    Accepted: --

    Published Online: Aug. 1, 2022

    The Author Email:

    DOI:10.16818/j.issn1001-5868.2022011305

    Topics