Skip to main content

Research Repository

Advanced Search

Content-sensitive superpixel generation with boundary adjustment.

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

Authors

Dong Zhang

Gang Xie

Zhe Zhang

Wenliang Bao

Xinying Xu



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.

Citation

ZHANG, D., XIE, G., REN, J., ZHANG, Z., BAO, W. and XU, X. 2020. Content-sensitive superpixel generation with boundary adjustment. Applied sciences [online], 10(9), article 3150. Available from: https://doi.org/10.3390/app10093150

Journal Article Type Article
Acceptance Date Apr 27, 2020
Online Publication Date Apr 30, 2020
Publication Date May 1, 2020
Deposit Date May 6, 2022
Publicly Available Date Mar 28, 2024
Journal Applied Sciences
Electronic ISSN 2076-3417
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 10
Issue 9
Article Number 3150
DOI https://doi.org/10.3390/app10093150
Keywords Content-sensitive; Superpixel; Boundary adjustment
Public URL https://rgu-repository.worktribe.com/output/1085499

Files




You might also like



Downloadable Citations