Adaptive fusion affinity graph with noise-free online low-rank representation for natural image segmentation

Abstract

Affinity graph-based segmentation methods have become a major trend in computer vision. The performance of these methods rely on the constructed affinity graph, with particular emphasis on the neighborhood topology and pairwise affinities among superpixels. However, these graph-based methods ignore the noisy data from images, that influence the accuracy of pairwise similarities. Multiscale combinatorial grouping and graph fusion also generate a higher computational complexity. In this paper, we propose an adaptive fusion affinity 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 an 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. Experimental results on BSD300, BSD500, MSRC, SBD, and PASCAL VOC show the effectiveness of our method in comparison with the state-of-the-art approaches. The code is available at https://github.com/Yangzhangcst/AFA-graph.