Automatic image segmentation with superpixels and image-level labels.

Xie, Xinlin; Xie, Gang; Xu, Xinying; Cui, Lei; Ren, Jinchang

Abstract

Automatically and ideally segmenting the semantic region of each object in an image will greatly improve the precision and efficiency of subsequent image processing. We propose an automatic image segmentation algorithm based on superpixels and image-level labels. The proposed algorithm consists of three stages. At the stage of superpixel segmentation, we adaptively generate the initial number of superpixels using the minimum spatial distance and the total number of pixels in the image. At the stage of superpixel merging, we define small superpixels and directly merge the most similar superpixel pairs without considering the adjacency, until the number of superpixels equals the number of groupings contained in image-level labels. Furthermore, we add a stage of reclassification of disconnected regions after superpixel merging to enhance the connectivity of segmented regions. On the widely used Microsoft Research Cambridge data set and Berkeley segmentation data set, we demonstrate that our algorithm can produce high-precision image segmentation results compared with the state-of-the-art algorithms.

Citation

XIE, X., XIE, G., XU, X., CUI, L. and REN, J. 2019. Automatic image segmentation with superpixels and image-level labels. IEEE access [online], 7, pages 10999-11009. Available from: https://doi.org/10.1109/ACCESS.2019.2891941

• Journal Article Type: Article
• Acceptance Date: Dec 27, 2018
• Online Publication Date: Jan 10, 2019
• Publication Date: Dec 31, 2019
• Deposit Date: Jul 16, 2024
• Publicly Available Date: Jul 16, 2024
• Journal: IEEE access.
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 10999-11009
• DOI: https://doi.org/10.1109/ACCESS.2019.2891941
• Keywords: Image segmentation; Superpixels; Image-level labels; Disconnected regions
• Public URL: https://rgu-repository.worktribe.com/output/2058960