Abstract
In this paper, we present a new image segmentation method using iterated graph cuts. In the standard graph cuts method, the data term is computed on the basis of the brightness/color distribution of object and background. In this case, some background regions with the brightness/color similar to the object may be incorrectly labeled as an object. We try to overcome this drawback by introducing a new data term that reduces the importance of brightness/color distribution. This reduction is realised by a new part that uses data from a residual graph that remains after performing the max-flow algorithm. According to the residual weights, we change the weights of t-links in the graph and find a new cut on this graph. This operation makes our method iterative. The results and comparison with other graph cuts methods are presented.
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Holuša, M., Sojka, E. (2013). Image Segmentation Using Iterated Graph Cuts with Residual Graph. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_23
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DOI: https://doi.org/10.1007/978-3-642-41914-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41913-3
Online ISBN: 978-3-642-41914-0
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