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Affinity graph-based segmentation methods have become a major trend incomputer vision. The performance of these methods relies on the constructedaffinity graph, with particular emphasis on the neighborhood topology andpairwise affinities among superpixels. Due to the advantages of assimilatingdifferent graphs, a multi-scale fusion graph has a better performance than asingle graph with single-scale. However, these methods ignore the noise fromimages which influences the accuracy of pairwise similarities. Multi-scalecombinatorial grouping and graph fusion also generate a higher computationalcomplexity. In this paper, we propose an adaptive fusion affinity graph(AFA-graph) with noise-free low-rank representation in an online manner fornatural image segmentation. An input image is first over-segmented intosuperpixels at different scales and then filtered by the proposed improvedkernel density estimation method. Moreover, we select global nodes of thesesuperpixels on the basis of their subspace-preserving presentation, whichreveals the feature distribution of superpixels exactly. To reduce timecomplexity while improving performance, a sparse representation of global nodesbased on noise-free online low-rank representation is used to obtain a globalgraph at each scale. The global graph is finally used to update a local graphwhich is built upon all superpixels at each scale. Experimental results on theBSD300, BSD500, MSRC, SBD, and PASCAL VOC show the effectiveness of AFA-graphin comparison with state-of-the-art approaches.

Adaptive Fusion Affinity Graph with Noise-free Online Low-rank Representation for Natural Image Segmentation