Unsupervised Multiple Object Segmentation of Multiview Images

Abstract

In this paper we propose an unsupervised multiview image segmentation algorithm, combining multiple image cues including color, depth, and motion. First, the interested objects are extracted by computing a saliency map based on the visual attention model. By analyzing the saliency map, we automatically obtain the number of foreground objects and their bounding boxes, which are used to initialize the segmentation algorithm. Then the optimal segmentation is calculated by energy minimization under the min-cut/max-flow theory. There are two major contributions in this paper. First, we show that the performance of graph cut segmentation depends on the user interactive initialization, while our proposed method provides robust initialization instead of the random user input. In addition, we propose a novel energy function with a locally adaptive smoothness term when constructing the graphs. Experimental results demonstrate that subjectively good segmentation results are obtained.