Abstract
This paper proposes a new technique for the problem of color image segmentation using GrabCut. GrabCut is considered as one of the semi-automatic segmentation techniques, since it requires user interaction for the initialization of the segmentation process, via dragging a rectangle around an object to extract it. This restricts GrabCut for bi-label segmentation, where the image cannot be segmented into more than two; foreground and background segments. In order to set up for multi-label segmentation, this paper presents the use of SOFM as a powerful unsupervised clustering technique for the GrabCut initialization process. This converts the GrabCut from a semi-automatic into a complete automatic segmentation technique. The use of different SOFM architectures for the process of image segmentation was tested for real experiments. Evaluation and comparison with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation quality and accuracy.
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Khattab, D., Ebied, H.M., Hussein, A.S., Tolba, M.F. (2015). Automatic GrabCut for Bi-label Image Segmentation Using SOFM. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_50
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DOI: https://doi.org/10.1007/978-3-319-11310-4_50
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11309-8
Online ISBN: 978-3-319-11310-4
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