Skip to main content

Data Graph Formulation as the Minimum-Weight Maximum-Entropy Problem

  • Conference paper
  • 1314 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9069))

Abstract

Consider a point-set coming from an object which was sampled using a digital sensor (depth range, camera, etc). We are interested in finding a graph that would represent that point-set according to some properties. Such a representation would allow us to match two objects (graphs) by exploiting topological properties instead of solely relying on geometrical properties. The Delaunay triangulation is a common out off-the-shelf strategy to triangulate a point-set and it is used by many researchers as the standard way to create the so called data-graph and despite its positive properties, there are also some drawbacks. We are interested in generating a graph with the following properties: the graph is (i) as unique as possible, (ii) and as discriminative as possible regarding the degree distribution. We pose a combinatorial optimization problem (Min-Weight Max-Entropy Problem) to build such a graph by minimizing the total weight cost of the edges and at the same time maximizing the entropy of the degree distribution. Our optimization approach is based on Dynamic Programming (DP) and yields a polynomial time algorithm.

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

Buying options

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Conrad, K.: Probability distributions and maximum entropy (2005), http://www.math.uconn.edu/~kconrad/blurbs/analysis/entropypost.pdf

  2. Cross, A.D.J., Hancock, E.R.: Graph matching with a dual-step em algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1236–1253 (1998), ISSN 0162-8828

    Google Scholar 

  3. de Sousa, S., Kropatsch, W.G.: Graph-based point drift: Graph centrality on the registration of point-sets. Pattern Recognition 48(2), 368–379 (2015), ISSN 0031-3203

    Google Scholar 

  4. Delaunay, B.N.: Sur la sphère vide. Bulletin of Academy of Sciences of the USSR, 793–800 (1934)

    Google Scholar 

  5. Dickinson, P.J., Bunke, H., Dadej, A., Kraetzl, M.: Matching graphs with unique node labels. Pattern Analysis and Applications 7(3), 243–254 (2004), ISSN 1433-7541

    Google Scholar 

  6. Erdös, T., Gallai, P.: Gráfok előírt fokszámú pontokkal. Matematikai Lapok 11, 264–274 (1960)

    Google Scholar 

  7. Gabriel, K.R., Sokal, R.R.: A new statistical approach to geographic variation analysis. Systematic Biology 18(3), 259–278 (1969)

    Google Scholar 

  8. Hakimi, S.: On realizability of a set of integers as degrees of the vertices of a linear graph. i. Journal of the Society for Industrial and Applied Mathematics 10(3), 496–506 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  9. Havel, V.: Poznámka o existenci konečných graf•367u. Časopis Pro Pěstování Matematiky 080(4), 477–480 (1955)

    MathSciNet  Google Scholar 

  10. Kocay, W., Kreher, D.L.: Graphs, Algorithms, and Optimization. Discrete Mathematics and Its Applications. Taylor & Francis (2004) ISBN 9780203489055

    Google Scholar 

  11. Lian, W., Zhang, L.: Rotation invariant non-rigid shape matching in cluttered scenes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 506–518. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  12. Luo, B., Hancock, E.R.: Iterative procrustes alignment with the {EM} algorithm. Image and Vision Computing 20(5-6), 377–396 (2002), ISSN 0262-8856

    Google Scholar 

  13. Ohrhallinger, S., Mudur, S.: An efficient algorithm for determining an aesthetic shape connecting unorganized 2d points. Computer Graphics Forum 32(8), 72–88 (2013), ISSN 1467-8659

    Google Scholar 

  14. Reeb, G.: Sur les points singuliers d’une forme de pfaff complétement intégrable ou d’une fonction numérique. C. R. Acad. Sci. Paris 222, 847–849 (1946)

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Samuel de Sousa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

de Sousa, S., Kropatsch, W.G. (2015). Data Graph Formulation as the Minimum-Weight Maximum-Entropy Problem. In: Liu, CL., Luo, B., Kropatsch, W., Cheng, J. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2015. Lecture Notes in Computer Science(), vol 9069. Springer, Cham. https://doi.org/10.1007/978-3-319-18224-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18224-7_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18223-0

  • Online ISBN: 978-3-319-18224-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics