Multilevel affinity graph for unsupervised image segmentation
Ang Li, Xiuying Wang, Ke Yan, Changyang Li, Dagan D. Feng · 2016
Unsupervised segmentation and contour detection remains a challenging task. In graph-based unsupervised segmentation, the formulation of the affinity graph is pivotal to segmentation performance. Conventional graph-based approaches often only define pixels as graph nodes, and may overlook important regional information. In this paper, we propose a novel scheme for affinity graph construction, where the affinity weight matrix unifies the association across pixel-wise nodes and multilevel region-wise nodes of different scales. Integrating the multilevel regional information, which is formulated using superpixels, into the affinity graph contributes to better capture of image intensity and color cues. Experimental evaluation of our approach on the BSDS500 dataset showed that our proposed method achieved the second best performance compared to other nine unsupervised state-of-art methods commonly used for comparison.