Comparison of different color spaces for image segmentation using graph-cut

Graph-cut optimization has been successfully applied in many image segmentation tasks. Within this framework color information has been extensively used as a perceptual property of objects to segment the foreground object from background. There are different representations of color in digital images, each with special characteristics. Previous work on segmentation lacks a systematic study of which color space is better suited for image segmentation. This work applies the Graph Cut algorithm for image segmentation based on five different, widespread color spaces and evaluates their performance on public benchmark datasets. Most of the tested color spaces lead to similar results. Segmentations based on L^*a^*b^* color space are of slightly higher or similar quality as all the other methods. In contrast, RGB-based segmentations are mostly worse than a segmentation based on any other tested color space.