AN ADAPTIVE AFFINITY GRAPH WITH SUBSPACE PURSUIT FOR NATURAL IMAGE SEGMENTATION

Graph-based segmentation methods have become a major trend in computer vision. Due to the advantages of assimilating different graphs, a multi-scale fusion graph have a better performance than a single graph with single-scale. However, it is not reliable to determine a principle of graph combination. In this paper, we propose an adaptive affinity graph with subspace pursuit (AASP-graph) for natural image segmentation. The input image is first over-segmented into superpixels at different scales. An improved affinity propagation clustering method is proposed to select global nodes of these superpixels adaptively. Then, a l(0)-graph at each scale is obtained by a sparse representation of global nodes based on subspace pursuit. The adjacency-graph is finally built upon all superpixels of each scale and updated by the l(0)-graph. Experimental results on the Berkeley segmentation database show the effectiveness of the proposed AASP-graph in comparison with state-of-the-art approaches.