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. © 2014 Springer International Publishing.
CITATION STYLE
Yuan, Z., Lu, T., & Shivakumara, P. (2014). A novel topic-level random walk framework for scene image co-segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8689 LNCS, pp. 695–709). Springer Verlag. https://doi.org/10.1007/978-3-319-10590-1_45
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