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A Novel Topic-Level Random Walk Framework for Scene Image Co-segmentation*
Zehuan Yuan1, Tong Lu1, and Palaiahnakote Shivakumara2
1National Key Laboratory of Software Novel Technology, Nanjing University, China
2Faculty of Computer Science and Information Technology, University of Malaya, Malaysia
Abstract. Image co-segmentation is popular with its ability to detour supervisory data by exploiting the common information in multiple images. In this paper, we aim at a more challenging branch called scene image co-segmentation, which jointly segments multiple images captured from the same scene into regions corresponding to their respective classes. We first put forward a novel representation named Visual Relation Network (VRN) to organize multiple segments, and then search for meaningful segments for every image through voting on the network. Scalable topic-level random walk is then used to solve the voting problem. Experiments on the benchmark MSRC-v2, the more difficult LabelMe and SUN datasets show the superiority over the state-of-the-art methods.
Keywords: Image co-segmentation, voting, random walk, link analysis
Electronic Supplementary Material:
LNCS 8689, p. 695 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014