A Graph Cuts Based Interactive Image Segmentation Method

Liu Jia; Wang Hong-qi

doi:10.3724/SP.J.1146.2007.00075

Volume 30 Issue 8

Jan. 2011

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Liu Jia, Wang Hong-qi. A Graph Cuts Based Interactive Image Segmentation Method[J]. Journal of Electronics & Information Technology, 2008, 30(8): 1973-1976. doi: 10.3724/SP.J.1146.2007.00075

Citation:

Liu Jia, Wang Hong-qi. A Graph Cuts Based Interactive Image Segmentation Method[J]. Journal of Electronics & Information Technology, 2008, 30(8): 1973-1976. doi: 10.3724/SP.J.1146.2007.00075

Liu Jia, Wang Hong-qi. A Graph Cuts Based Interactive Image Segmentation Method[J]. Journal of Electronics & Information Technology, 2008, 30(8): 1973-1976. doi: 10.3724/SP.J.1146.2007.00075

Citation:

Liu Jia, Wang Hong-qi. A Graph Cuts Based Interactive Image Segmentation Method[J]. Journal of Electronics & Information Technology, 2008, 30(8): 1973-1976. doi: 10.3724/SP.J.1146.2007.00075

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A Graph Cuts Based Interactive Image Segmentation Method

doi: 10.3724/SP.J.1146.2007.00075

Received Date: 2007-01-11
Rev Recd Date: 2007-09-11
Publish Date: 2008-08-19

Abstract

Abstract

Interactive image segmentation methods have recently gained more and more attentions. A new interactive segmentation method is proposed based on the graph cuts. It combines several image features like texture, color and edge together through a probabilistic model. Texture and color features are modeled with histograms. Dimensionality reduction in feature space is achieved with a fisher discriminant criterion based on texton. The global optimal segmentation can be efficiently computed via graph cuts. Efficiency and accuracy of the method is demonstrated on aerial image segmentation and some other applications.

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References

Rother C, Kolmogorov V, and Blake A. GrabCut interactive foreground extraction using iterated graph cuts[J].ACM Trans. Graphics, Los Angeles.2004, 23(3):309-314[2]Boykov Y and Jolly M P. Interactive graph cuts for optimalboundary region segmentation of objects in N-D images.International Conference on Computer Vision, Vancouver,BC, Canada, 2001, 1: 105-112.[3]Malik J, Belongie S, Shi J, and Leung T. Textons, contoursand regions: cue integration in image segmentation.International Conference on Computer Vision, Kerkyra,Corfu, Greece, 1999, 2: 918-925.[4]Leung T and Malik J. Representing and recognizing thevisual appearance of materials using three-dimensionaltextons[J].International Journal of Computer Vision.2001,43(1):29-44[5]Varma M and Zisserman A. Unifying statistical textureclassification frameworks. Image and Vision Computing, 2004,22(14): 1175-1183.[6]Stan Z L. Markov Random Field Modeling in Image Analysis.Inc. Secaucus, NJ, USA, Springer-Verlag New York, 2001, 18.[7]Kolmogorov V and Zabih R. What energy functions can beminimized via graph cuts? IEEE Trans[J].on Pattern Analysisand Machine Intelligence.2004, 26(2):147-159[8]Thomas H C, Charles E L, Ronald L R, and Clifford S.Introduction to Algorithm, 2nd Edition. Cambridge, MA,USA, The MIT Press, 2001, 656.[9]Boykov Y and Kolmogorov V. An experimental comparisonof min-cut/max-flow algorithms for energy minimization invision[J].IEEE Trans. on Pattern Analysis and MachineIntelligence.2004, 26(9):1124-1137

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