A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation (Advances in Applied Mathematics and Mechanics, 2022)
Color image segmentation is crucial in image processing and computer vision. Most traditional segmentation methods simply regard an RGB color image asthe direct combination of the three monochrome images and ignore the inherent colorstructures within channels, which contain some key feature information of the image.To better describe the relationship of color channels, we introduce a quaternion-basedregularization that can reflect the image characteristics more intuitively. Our modelcombines the idea of the Mumford-Shah model-based two-stage segmentation methodand the Saturation-Value Total Variation regularization for color image segmentation.The new strategy first extracts features from the color image and then subdivides theimage in a new color feature space which achieves better performance than methods in RGB color space. Moreover, to accelerate the optimization process, we use anew primal-dual algorithm to solve our novel model. Numerical results demonstrateclearly that the performance of our proposed method is excellent.