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Image Segmentation Based on Supernodes and Region Size Estimation

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 5259))

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Abstract

A kind of self-adaptive image segmentation algorithm is introduced in this paper, and of which the main frame is based on Graph Structure. Two contributions have been made in our work. First, super-pixels act as the graph nodes for computational efficiency, at the same time, more local features could be abstracted from the pre-segmented image. Second, region size is estimated during the process to reduce interaction between human and computer. Experimental results demonstrate that the improved method is unsupervised and could give satisfactory segmentation.

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References

  1. Vincent, L., Soille, P.: Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)

    Article  Google Scholar 

  2. Wang, Y., Teoh, E.K.: Object Contour Extraction Using Adaptive B-Snake Model. Journal of Mathematical Imaging and Vision 24(3), 295–306 (2006)

    Article MathSciNet  Google Scholar 

  3. Comaniciu, D., Meer, P.: Mean Shift Analysis and Application. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2, pp. 1197–1203 (1999)

    Google Scholar 

  4. Lu, F., Fu, Z., Robles-Kelly, A.: Efficient graph cuts for multiclass Interactive Image Segmentation. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 134–144. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  5. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)

    Article  Google Scholar 

  6. Navon, E., Miller, O., Averbuch, A.: Color image segmentation based on adaptive local thresholds. Image and Vision Computing 23(1), 69–85 (2005)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, South China Univ. of Tech., 510640, Guangzhou, China

    Yuan Yuan & Lihong Ma

  2. National Lab of Pattern Recognition, Inst. Automation, Chinese Academy of Science, 100080, Beijing, China

    Hanqing Lu

Authors
  1. Yuan Yuan

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  2. Lihong Ma

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  3. Hanqing Lu

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Editor information

Editors and Affiliations

  1. DGA/D4S/MRIS, 94114, Arcueil, France

    Jacques Blanc-Talon

  2. Ecole Centrale de Marseille, 13451, Marseille, France

    Salah Bourennane

  3. Ghent University, 9000, Gent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre, NSW 1710, Sydney, Australia

    Dan Popescu

  5. University of Antwerp, 2610, Wilrijk, Belgium

    Paul Scheunders

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© 2008 Springer-Verlag Berlin Heidelberg

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Yuan, Y., Ma, L., Lu, H. (2008). Image Segmentation Based on Supernodes and Region Size Estimation. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_62

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