Effect of Different Color Spaces on Deep Image Segmentation

Image segmentation is an important application in computer vision, proposed to partition an image into meaningful regions on a specific criterion. In recent days, image segmentation tasks have achieved state of the art performance using deep neural and fully connected networks. The datasets used for the segmentation task mainly consist of image data in RGB color space and the deep segmentation architectures are trained without modifying the color space. In this study, the importance of color space is investigated and the obtained results show that the color space can affect the segmentation performance remarkably. Certain regions of interest in images belonging to a particular domain can be segmented better when represented in a certain form of color space. To explore on this two datasets from medical and satellite imagery are considered. The UNET model is modified to accept images as a combination of color spaces and is trained to segment the colonoscopy images for polyps and satellite images for roads under individual and combination of color spaces. Experiments show that the performance of polyp segmentation is better when a combination of HSV+YCbCr color space is considered. Road segmentation in satellite imagery is better in LAB+HSV color space.