Skip to main content
SpringerLink
Log in

Image Segmentation by Image Analogies

  • Conference paper
Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications (CompIMAGE 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8641))

Abstract

In this paper we propose a new technique for image segmentation based on contour detection using image analogies principle. A set of artificial patterns are used to locate contours of any query image. Each pattern allow the location of contours corresponding to specific intensity variation. Boundaries are extracted based on the properties of located contours. In addition, elementary regions derived from the motion of contours in images are located and combined jointly with the boundaries for image segmentation. Experiments are conducted and the obtained results are presented and discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alpert, S., Galun, M., Basri, R., Brandt, A.: Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (June 2007)

    Google Scholar 

  2. Ashikhmin, M.: Synthesizing natural textures. In: Proceedings of 2001 ACM Symposium on Interactive 3D Graphics (2001)

    Google Scholar 

  3. Ashikhmin, M.: Fast texture transfer. IEEE Computer Graphics and Applications 23(4), 38–43 (2003)

    Article  Google Scholar 

  4. Bellili, A., Larabi, S., Robertson, N.M.: Outlines of objects detection by analogy. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part I. LNCS, vol. 8047, pp. 385–392. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  5. Bhat, P., Ingram, S., Turk, G.: Geometric texture synthesis by example. In: Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing, Nice (2004)

    Google Scholar 

  6. Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.L.: Color image segmentation: advances and prospects. In: Pattern Recognition (2001)

    Google Scholar 

  7. Cheng, L., Vishwanathan, S., Zhang, X.: Consistent image analogies using semi-supervised learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage (2008)

    Google Scholar 

  8. Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning Low-Level Vision. International Journal of Computer Vision 40(1), 25–47 (2000)

    Article  MATH  Google Scholar 

  9. Hertzmann, A., Jacobs, C., Oliver, N., Curless, B., Salesin, D.: Image analogies. In: Proceedings of the 28th Annual ACM Conference on Computer Graphics and Interactive Techniques, New York (2001)

    Google Scholar 

  10. Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Seitz, S.M.: Image analogies. In: SIGGRAPH Conference Proceedings, pp. 327–340 (2001)

    Google Scholar 

  11. Hertzmann, A., Oliver, N., Curless, B., Seitz, S.M.: Curve analogies. In: EGRW 2002 Proceedings of the 13th Eurographics Workshop on Rendering, Switzerland (2002)

    Google Scholar 

  12. Lackey, J.B., Colagrosso, M.D.: Supervised segmentation of visible human data with image analogies. In: Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications (2004)

    Google Scholar 

  13. Larabi, S., Robertson, N.M.: Contour detection by image analogies. In: Bebis, G., et al. (eds.) ISVC 2012, Part II. LNCS, vol. 7432, pp. 430–439. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  14. Nikhil, R.P., Sankar, K.P.: A review on image segmentation techniques. In: Pattern Recognition (1993)

    Google Scholar 

  15. Haralick, R.M., Shapiro: Image segmentation techniques. In: Computer Vision, Graphics and Image Processing (1985)

    Google Scholar 

  16. Sykora, D., Burianek, J., Zara, J.: Unsupervised colorization of black-and-white cartoons. In: Proceedings of the 3rd Int. Symp. Non-photorealistic Animation and Rendering, pp. 121–127 (2004)

    Google Scholar 

  17. Wang, G., Wong, T., Heng, P.: Deringing cartoons by image analogies. ACM Transactions on Graphics 25(4), 1360–1379 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, USTHB University, BP 32 El Alia, Algiers, Algeria

    Asma Bellili & Slimane Larabi

Authors
  1. Asma Bellili
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Slimane Larabi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA

    Yongjie Jessica Zhang

  2. Faculdade de Engenharia, Departamento de Engenharia Mecânica, Universidade do Porto, Rua Dr. Roberto Frias, S/N, 4200-465, Porto, Portugal

    João Manuel R. S. Tavares

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Bellili, A., Larabi, S. (2014). Image Segmentation by Image Analogies. In: Zhang, Y.J., Tavares, J.M.R.S. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2014. Lecture Notes in Computer Science, vol 8641. Springer, Cham. https://doi.org/10.1007/978-3-319-09994-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-09994-1_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09993-4

  • Online ISBN: 978-3-319-09994-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation