Content-sensitive superpixel generation with boundary adjustment.

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

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: Jun 6, 2022
• 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