Colour image segmentation based on a convex K‐means approach. Issue 8 (22nd January 2021)

• Record Type: Journal Article
• Authors: Wu, Tingting; Gu, Xiaoyu; Shao, Jinbo; Zhou, Ruoxuan; Li, Zhi
• Is Part Of: IET image processing. Volume 15:Issue 8(2021)
• Page Start: 1596
• Page End: 1606
• Publication Date: 2021-01-22
• Subjects: Optimisation techniques -- Optical, image and video signal processing -- Optimisation techniques -- Computer vision and image processing techniques; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12128
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: Image segmentation is a fundamental and challenging task in image processing and computer vision. The colour image segmentation is attracting more attention as the colour image provides more information than the grey image. A variational model based on a convex K‐means approach to segment colour images is proposed. The proposed variational method uses a combination of l 1 and l 2 regularizers to maintain edge information of objects in images while overcoming the staircase effect. Meanwhile, our one‐stage strategy is an improved version based on the smoothing and thresholding strategy, which contributes to improving the accuracy of segmentation. The proposed method performs the following steps. First, the colour set which can be determined by human or the K‐means method is specified. Second, a variational model to obtain the most appropriate colour for each pixel from the colour set via convex relaxation and lifting is used. The Chambolle–Pock algorithm and simplex projection are applied to solve the variational model effectively. Experimental results and comparison analysis demonstrate the effectiveness and robustness of the method.