In this paper, an automatic RGBD object segmentation method is described. The method integrates depth feature with the cues from RGB images and then uses maximal similarity based region merging (MSRM) method to obtain the segmentation results. Firstly, the depth information is fused to the simple linear iterative clustering (SLIC) method so as to produce superpixels whose boundaries are well adhered to the edges of the natural image. Meanwhile, the depth prior is also incorporated into the saliency estimation, which helps a more accurate localization of representative object and background seeds. By introducing the depth cue into the region merging rule, the maximal geometry weighted similarity (MGWS) is considered, and the resulting segmentation framework has the ability to handle the complex image with similar colour appearance between object and background. Extensive experiments on public RGBD image datasets show that our proposed approach can reliably and automatically provide very promising segmentation results.
Object segmentation by saliency-seeded and spatial-weighted region merging
