An Enhanced Affinity Graph for Image Segmentation

Guodong SUN  Kai LIN  Junhao WANG  Yang ZHANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.5   pp.1073-1080
Publication Date: 2019/05/01
Publicized: 2019/02/04
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7322
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
image segmentation,  superpixels,  sparse reconstruction,  enhanced affinity graph,  spectral clustering,  

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This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.