Abstract
This paper presents a novel selective constraint propagation method for constrained image segmentation. In the literature, many pairwise constraint propagation methods have been developed to exploit pairwise constraints for cluster analysis. However, since these methods mostly have a polynomial time complexity, they are not much suitable for segmentation of images even with a moderate size, which is equal to cluster analysis with a large data size. In this paper, we thus choose to perform pairwise constraint propagation only over a selected subset of pixels, but not over the whole image. Such a selective constraint propagation problem is then solved by an efficient graph-based learning algorithm. Finally, the selectively propagated constraints are exploited based on \(L_1\)-minimization for normalized cuts over the whole image. The experimental results show the promising performance of the proposed method.
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Acknowledgments
This work was partially supported by National Natural Science Foundation of China (61573363 and 61573026), 973 Program of China (2014CB340403 and 2015CB352502), the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (15XNLQ01), and IBM Global SUR Award Program.
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Han, P., Liu, G., Huang, S., Yuan, W., Lu, Z. (2016). Segmentation with Selectively Propagated Constraints. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_65
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DOI: https://doi.org/10.1007/978-3-319-46672-9_65
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