An Adaptive Affinity Graph with Subspace Pursuit for Natural Image Segmentation
Yang Zhang, Huiming Zhang, Yanwen Guo, Kai Yan Lin, Jingwu He · 2019
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 L0-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 L0-graph. Experimental results on the Berkeley segmentation database show the effectiveness of the proposed AASP-graph in comparison with state-of-the-art approaches.