Abstract
The existing cosegmentation methods use intra-group information to extract a common object from a single image group. Observing that in many practical scenarios there often exist multiple image groups with distinct characteristics but related to the same common object, in this paper we propose a multi-group image cosegmentation framework, which not only discoveries intra-group information within each image group, but also transfers the inter-group information among different groups so as to more accurate object priors. Particularly, we formulate the multi-group cosegmentation task as an energy minimization problem. Markov random field (MRF) segmentation model and dense correspondence model are used in the model design and the Expectation-Maximization algorithm (EM) is adapted to solve the optimization. The proposed framework is applied on three practical scenarios including image complexity based cosegmentation, multiple training group cosegmentation and multiple noise image group cosegmentation. Experimental results on four benchmark datasets show that the proposed multi-group image cosegmentation framework is able to discover more accurate object priors and significantly outperform state-of-the-art single-group image cosegmentation methods.
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Acknowledgement
This work was supported in part by the Major State Basic Research Development Program of China (973 Program 2015CB351804), NSFC (No. 61271289), the Singapore National Research Foundation under its IDM Futures Funding Initiative and administered by the Interactive & Digital Media Programme Office, Media Development Authority, the Ph.D. Programs Foundation of Ministry of Education of China (No. 20110185110002), and by The Program for Young Scholars Innovative Research Team of Sichuan Province, China (No. 2014TD0006).
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Meng, F., Cai, J., Li, H. (2015). On Multiple Image Group Cosegmentation. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_17
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DOI: https://doi.org/10.1007/978-3-319-16817-3_17
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