Generative Image Segmentation
using Random Walks with Restart



We consider the problem of multi-label, supervised image segmentation when an initial labeling of some pixels is given. In this paper, we propose a new generative image segmentation algorithm for reliable multi-label segmentations in natural images. In contrast to most existing algorithms which focus on the inter-label discrimination, we address the problem of finding the generative model for each label. The primary advantage of our algorithm is that it produces very good segmentation results under two difficult problems: the weak boundary problem and the texture problem. Moreover, single-label image segmentation is possible. These are achieved by designing the generative model with the Random Walks with Restart (RWR). Experimental results with synthetic and natural images demonstrate the relevance and accuracy of our algorithm.

paper thumbnail


ECCV 2008 paper. (pdf, 2.05MB) Poster. (pdf, 2.6MB)


Tae Hoon Kim, Kyoung Mu Lee, Sang Uk Lee. Generative Image Segmentation Using Random Walks with Restart, European Conference on Computer Vision (ECCV), 2008


code. (zip, 25KB) dataset. (zip, 1.9MB)


This research is supported in part by: