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An Improved Interactive Color Image Segmentation Using Region-Based Graph Cuts

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The problem of efficient interactive extraction of a foreground object in a complex environment is the primary one in image processing and computer vision. The segmentation method based on graph cuts has been studied over the recent years. There are two main drawbacks of these studies: decrease in performance when the foreground and the background have similar colors, and long computing time when the image is large. In this paper, we present a new foreground objects extraction method using a region-based graph cuts algorithm. The image is pre-segmented into a large number of small partitions using the mean shift (MS) method. We use the partitions to represent the nodes in the graph instead of pixels. This approach can reduce the optimization time, which is closely related to the number of nodes and edges in the graph. Compared with the pixel-based method, our method can yield an excellent performance and exhibit a faster speed.
Rocznik
Strony
477--488
Opis fizyczny
Bibliogr. 13 poz., il.
Twórcy
autor
autor
autor
autor
  • Digital Engineering & Simulation Research Center, Huazhong University of Science and Technology, Wuhan 430074, P.R. China, tianyuancolor@gmail.com
Bibliografia
  • [1] D. M. Greig, B. T. Porteous and A. H. Seheult. Exact Maximum A Posteriori Estimation for Binary Images. Journal of the Royal Statistical Society, Series B (Methodological), Vol. 51, No. 2, pp. 271-279.
  • [2] Yizong Cheng. Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 8, pp. 790-799, August. 1995.
  • [3] Jianbo Shi and Jitendra Malik. Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 888-905, August. 2000
  • [4] Yuri Y. Boykov and Marie-Pierre Jolly. Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images. In International Conference on Computer Vision, Vol. 1, pp. 105-112. 2001.
  • [5] Song Wang and Jeffrey Mark Siskind. Image Segmentation with Minimum Mean Cut. Proceedings of the IEEE International Conference on Computer Vision, Vol. 1, pp. 517-524. 2001.
  • [6] Yuri Boykov and Vladimir Kolmogorov. An experimental comparison of min-cut/max-fiow algorithms for energy minimization in computer vision. In International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, Vol. 2134 of LNCS, pp. 359-374. Springer-Verlag, September. 2001.
  • [7] D. Comaniciu and P. Meer: Mean shift: A robust approach toward feature space analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, pp. 603-619, May. 2002.
  • [8] Song Wang and Jeffrey Mark Siskind. Image Segmentation with Ratio Cut. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 6, pp. 675-690, June. 2003.
  • [9] Dorin Comaniciu. An Algorithm for Data-Driven Bandwidth Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 2, pp. 281-288, February. 2003.
  • [10] Yin Li, Jian Sun, Chi-Keung Tang, and Heung-Yeung Shum. Lazy Snapping. ACM Transaction on Graphics, Vol. 23, No. 3, pp. 303-308, April. 2004.
  • [11] Casten Rother, Vladimir Kolmogorov, and Andrew Blake. "GrabCut" - Interactive Foreground Extraction using Iterated Graph Cuts. ACM Transaction on Graphics, Vol. 23, No. 3, pp. 309-314, April. 2004.
  • [12] Olivier Juan and Yuri Boykov. Active Graph Cuts. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 1023-1029, June.
  • [13] T. Hosaka, T. Kobayashi and N. Otsu. Image Segmentation Using MAP-MRF Estimation and Support Vector Machine. Interdisciplinary Information Sciences, Vol. 13, No. 1, pp. 33-42. 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA0-0042-0035
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