An Enhanced Affinity Graph for Image Segmentation

Abstract

This paper proposes an enhanced affinity graph (EAgraph) for image segmentation. Firstly, the original image is oversegmented 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.

Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation

“…Based on the investigation on the property of the elliptically contoured distributions of generalized multivariate analysis, it selected the correct model order in a different way that needed less operation times and less sensitive to the initial value of EM. Sun [21] proposed an enhanced affinity graph for image segmentation. And other researchers proposed image segmentation methods [13,17,27].…”

A novel NAM‐based image segmentation using hierarchical density‐based spatial clustering

“…The graph-based algorithms which force on creating a reliable graph are another type of region-based algorithms. Algorithms such as the graph cut proposed by Wang [18,19] and Sun [20] converted image segmentation problems into an energy minimization process. Felzenszwalb and Huttenlocher proposed the GBIS [21] algorithm using a minimum spanning tree (MST) to construct the graph model.…”

“…Clearly, the effectiveness of the graph-based algorithms relies on the graph represented by the affinity matrix. Some algorithms such as Sun [20] and Zhang [27,28] focused on constructing an effective affinity matrix.…”