Color image segmentation based on a convex K-means approach

TL;DR

This paper introduces a convex K-means based variational model for color image segmentation that preserves edges and reduces artifacts, improving accuracy through a one-stage smoothing and thresholding strategy.

Contribution

It proposes a novel convex relaxation approach combining $l_1$ and $l_2$ regularizers for more accurate and robust color image segmentation.

Findings

Effective edge preservation in segmentation results

Reduced staircase artifacts compared to traditional methods

Demonstrated robustness across various images

Abstract

Image segmentation is a fundamental and challenging task in image processing and computer vision. The color image segmentation is attracting more attention due to the color image provides more information than the gray image. In this paper, we propose a variational model based on a convex K-means approach to segment color images. 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, we specify the color set which can be determined by human or the K-means method. Second, we use a variational model to obtain the most appropriate color for each pixel from the color set via convex relaxation and lifting. 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 our method.