Open AccessColour image segmentation based on a convex K‐means approach
Author(s) -
Wu Tingting,
Gu Xiaoyu,
Shao Jinbo,
Zhou Ruoxuan,
Li Zhi
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12128
Subject(s) - artificial intelligence , image segmentation , segmentation , thresholding , computer science , computer vision , scale space segmentation , regular polygon , variational method , smoothing , segmentation based object categorization , image (mathematics) , image processing , robustness (evolution) , mathematics , pattern recognition (psychology) , mathematical analysis , biochemistry , chemistry , geometry , gene
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.