RGB-D image segmentation using superpixel and multi-feature fusion graph theory

Abstract

It is difficult to obtain accurate segmentation results for a color map when there are shadows, low-contrast edges, or blurred regions in the image. The depth discontinuity of the image provides useful information for the identification of object boundaries. In this paper, a color and depth image (RGB-D) image segmentation method based on superpixels and multi-feature fusion graph theory is proposed. The method consists of two stages: (1) superpixel segmentation stage and (2) graph-based superpixel merge stage. Color information and depth information are combined to establish a Euclidean distance metric for the pixels, and superpixels are obtained through iterative clustering. A multi-feature fusion adjacent superpixel similarity measurement method based on the RGB-D data is proposed. A graph-based energy function is established, and a label cost is introduced in the energy function to eliminate redundant labels. We carried out many experiments on the RGB-D image database, and the results show that our superpixel segmentation method and graph-based superpixel merging method not only have higher segmentation accuracy than the existing methods, but also have advantages in terms of running time and memory consumption.