Unsupervised RGB-D image segmentation using joint clustering and region merging

Recent advances in imaging sensors, such as Kinect, provide access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-axial clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database V2 (NYUD2) and compare with existing unsupervised RGB-D segmentation methods. Results show that our method is comparable with the state of the art methods. Moreover, it opens interesting perspectives for fusing color and geometry in an unsupervised manner.