  Affinity graph-based segmentation methods have become a major trend in
computer vision. The performance of these methods relies on the constructed
affinity graph, with particular emphasis on the neighborhood topology and
pairwise affinities among superpixels. Due to the advantages of assimilating
different graphs, a multi-scale fusion graph has a better performance than a
single graph with single-scale. However, these methods ignore the noise from
images which influences the accuracy of pairwise similarities. Multi-scale
combinatorial grouping and graph fusion also generate a higher computational
complexity. In this paper, we propose an adaptive fusion affinity graph
(AFA-graph) with noise-free low-rank representation in an online manner for
natural image segmentation. An input image is first over-segmented into
superpixels at different scales and then filtered by the proposed improved
kernel density estimation method. Moreover, we select global nodes of these
superpixels on the basis of their subspace-preserving presentation, which
reveals the feature distribution of superpixels exactly. To reduce time
complexity while improving performance, a sparse representation of global nodes
based on noise-free online low-rank representation is used to obtain a global
graph at each scale. The global graph is finally used to update a local graph
which is built upon all superpixels at each scale. Experimental results on the
BSD300, BSD500, MSRC, SBD, and PASCAL VOC show the effectiveness of AFA-graph
in comparison with state-of-the-art approaches.
