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Proximity Graphs Based Multi-scale Image Segmentation

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Advances in Visual Computing (ISVC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5358))

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Abstract

We present a novel multi-scale image segmentation approach based on irregular triangular and polygonal tessellations produced by proximity graphs. Our approach consists of two separate stages: polygonal seeds generation followed by an iterative bottom-up polygon agglomeration. We employ constrained Delaunay triangulation combined with the principles known from visual perception to extract an initial irregular polygonal tessellation of the image. These initial polygons are built upon a triangular mesh composed of irregular sized triangles, whose spatial arrangement is adapted to the image content. We represent the image as a graph with vertices corresponding to the built polygons and edges reflecting polygon relations. The segmentation problem is then formulated as Minimum Spanning Tree (MST) construction. We build a successive fine-to-coarse hierarchy of irregular polygonal partitions by an iterative graph contraction. It uses local information and merges the polygons bottom-up based on local region- and edge- based characteristics.

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References

  1. Bhandarkar, S.M., Zeng, X.: Evolutionary approaches to figure-ground separation. Applied Intelligence 11, 187–212 (1999)

    Article  Google Scholar 

  2. Boyer, K.L., Sarkar, S. (eds.): Perceptual Organization for Artificial Vision Systems. Kluwer Acad. Publ., Dordrecht (2000)

    Google Scholar 

  3. Boykov, Y., Veksler, O., Zabin, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. On Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)

    Article  Google Scholar 

  4. Canny, J.: A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)

    Article  Google Scholar 

  5. Felzenszwalb, P.F., Huttenlocher, D.P.: Image segmentation using local variation. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 98–104 (1998)

    Google Scholar 

  6. Haxhimusa, Y., Kropatsch, W.G.: Segmentation graph hierarchies. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 343–351. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. Haxhimusa, Y., Ion, A., Kropatsch, W.G.: Comparing hierarchies of segmentations: Humans, normalized cut, and minimum spanning tree. In: Digital Imaging and Pattern Recognition, pp. 95–103 (2006)

    Google Scholar 

  8. Herault, L., Horaud, R.: Figure-ground discrimination: a combinatorial optimization approach. IEEE Trans. On Pattern Analysis and Machine Intelligence 15(9), 899–914 (1993)

    Article  Google Scholar 

  9. Horowitz, S.L., Pavlidis, T.: Picture segmentation by a tree traversal algorithm. Journal of the Association for Computing Machinery 23(2), 368–388 (1976)

    Article  MATH  Google Scholar 

  10. Jolion, J.-M., Montanvert, A.: The adaptive pyramid, a framework for 2D image analysis. CVGIP: Image Understanding 55(3), 339–348 (1992)

    Article  MATH  Google Scholar 

  11. Kropatsch, W.G., Haxhimusa, Y., Ion, A.: Multiresolution image segmentation in graph pyramids. In: Kandel, A., Bunke, H.H., Last, M. (eds.) Applied Graph Theory in Computer Vision and Pattern Recognition, vol. 52, pp. 3–42 (2007)

    Google Scholar 

  12. Montanvert, A., Meer, P., Rosenfeld, A.: Hierarchical image analysis using irregular tesselations. IEEE Trans. on Pattern Analysis and Machine Intelligence 13(4), 307–316 (1991)

    Article  Google Scholar 

  13. Prasad, L., Skourikhine, A.N.: Vectorized image segmentation via trixel agglomeration. Pattern Recognition 39(4), 501–514 (2006)

    Article  MATH  Google Scholar 

  14. Prasad, L., Skourikhine, A.N.: Vectorized image segmentation via trixel agglomeration. Pattern Recognition, U.S. Patent No. 7127104 (2006)

    Google Scholar 

  15. Sarkar, S., Soundararajan, P.: Supervised learning of large perceptual organization: graph spectral partitioning and learning automata. IEEE Trans. On Pattern Analysis and Machine Intelligence 22(5), 504–525 (2000)

    Article  Google Scholar 

  16. Shewchuk, J.R.: Triangle: engineering a 2D quality mesh generator and Delaunay triangulator. LNCS, vol. 1148, pp. 203–222. Springer, Heidelberg (1996)

    Google Scholar 

  17. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)

    Article  Google Scholar 

  18. Wertheimer, M.: Principles of perceptual organization. In: Beardslee, D., Wertheimer, M. (eds.) Readings in Perception. Van Nostrand, D. Princeton, NJ, pp. 115–135 (1958)

    Google Scholar 

  19. Xu, Y., Uberbacher, E.C.: 2D image segmentation using minimum spanning trees. Image and Vision Computing 15, 47–57 (1997)

    Article  Google Scholar 

  20. Yu, S.X.: Segmentation using multiscale cues. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 247–254 (2004)

    Google Scholar 

  21. Zahn, C.T.: Graph-theoretic methods for detecting and describing gestalt clusters. IEEE Transactions on Computers 20(1), 68–86 (1971)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. MS D436, Space and Remote Sensing Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA

    Alexei N. Skurikhin

Authors
  1. Alexei N. Skurikhin
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

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© 2008 Springer-Verlag Berlin Heidelberg

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Skurikhin, A.N. (2008). Proximity Graphs Based Multi-scale Image Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_29

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  • DOI: https://doi.org/10.1007/978-3-540-89639-5_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89638-8

  • Online ISBN: 978-3-540-89639-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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