Abstract
This paper presents an interactive object segmentation approach using graph cut and contour refinement, which can accurately extract any user-interested objects from natural images. Using the user-specified scribbles as the interactive input, the initial object segmentation result is obtained under the framework of graph cut. However, due to the problem of color distribution in some images, in which the color distributions of foreground and background are similar, it is nontrivial to achieve an acceptable segmentation quality using one-shot graph cut. Then, an interactive contour refinement scheme is exploited to correct inaccurate object contours to meet the user’s requirement. Experimental results on a variety of images demonstrate the better segmentation performance of our approach.
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References
Boykov, Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: Proc. IEEE ICCV, pp. 105–112 (July 2001)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. International Journal of Computer Vision 70(2), 109–131 (2006)
Rother, C., Kolmogorov, V., Blake, A.: GrabCut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics 23, 309–314 (2004)
Price, B.L., Morse, B., Cohen, S.: Geodesic graph cut for interactive image segmentation. In: Proc. IEEE CVPR, pp. 3161–3168 (June 2010)
Shi, R., Liu, Z., Xue, Y., Zhang, X.: Interactive object segmentation using iterative adjustable graph cut. In: Proc. IEEE VCIP, pp. 1–4 (November 2011)
Boykov, Y., Kolmogorov, V.: An experimental comparison of mincut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1124–1137 (2004)
Tran, T., Vo, P., Le, B.: Combining color and texture for a robust interactive segmentation algorithm. In: Proc. IEEE RIVF, pp. 1–4 (November 2010)
Ning, J., Zhang, L., Zhang, D., Wu, C.: Interactive image segmentation by maximal similarity based region merging. Pattern Recognition 43(2), 445–456 (2010)
Geng, X., Zhao, J.: Interactive image segmentation with conditional random fields. In: Proc. Int. Conf. Natural Computation, vol. 2, pp. 96–101 (November 2008)
Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90(7), 1151–1163 (2002)
Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.-Y.: Learning to detect a salient object. In: Proc. IEEE CVPR, pp. 1–8 (June 2007)
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© 2012 Springer-Verlag Berlin Heidelberg
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Shen, M., Zha, L., Liu, Z., Luo, S. (2012). Interactive Object Segmentation Using Graph Cut and Contour Refinement. In: Zhang, W., Yang, X., Xu, Z., An, P., Liu, Q., Lu, Y. (eds) Advances on Digital Television and Wireless Multimedia Communications. Communications in Computer and Information Science, vol 331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34595-1_15
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DOI: https://doi.org/10.1007/978-3-642-34595-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34594-4
Online ISBN: 978-3-642-34595-1
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