  This paper proposes an affinity fusion graph framework to effectively connect
different graphs with highly discriminating power and nonlinearity for natural
image segmentation. The proposed framework combines adjacency-graphs and kernel
spectral clustering based graphs (KSC-graphs) according to a new definition
named affinity nodes of multi-scale superpixels. These affinity nodes are
selected based on a better affiliation of superpixels, namely
subspace-preserving representation which is generated by sparse subspace
clustering based on subspace pursuit. Then a KSC-graph is built via a novel
kernel spectral clustering to explore the nonlinear relationships among these
affinity nodes. Moreover, an adjacency-graph at each scale is constructed,
which is further used to update the proposed KSC-graph at affinity nodes. The
fusion graph is built across different scales, and it is partitioned to obtain
final segmentation result. Experimental results on the Berkeley segmentation
dataset and Microsoft Research Cambridge dataset show the superiority of our
framework in comparison with the state-of-the-art methods. The code is
available at https://github.com/Yangzhangcst/AF-graph.
