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Graph Embedding for Pattern Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6388))

Abstract

This is the report of the first contest on Graph Embedding for Pattern Recognition, hosted at the ICPR2010 conference in Istanbul. The aim is to define an effective algorithm to represent graph-based structures in terms of vector spaces, to enable the use of the methodologies and tools developed in the statistical Pattern Recognition field. For this contest, a large dataset of graphs derived from three available image databases has been constructed, and a quantitative performance measure has been defined. Using this measure, the algorithms submitted by the contest participants have been experimentally evaluated.

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References

  1. The Open Source Computer Vision library, http://opencv.willowgarage.com/wiki/

  2. Bai, X., Yu, H., Hancock, E.R.: Graph matching using spectral embedding and alignment. In: 17th Int. Conference on Pattern Recognition, pp. 398–401 (2004)

    Google Scholar 

  3. Borgwardt, K., Kriegel, H.P.: Shortest-path kernels on graphs. In: Proc. 5th Int. Conference on Data Mining, pp. 74–81 (2005)

    Google Scholar 

  4. Emms, D., Wilson, R., Hancock, E.R.: Graph embedding using quantum commute times. In: Escolano, F., Vento, M. (eds.) GbRPR 2007. LNCS, vol. 4538, pp. 371–382. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  5. Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. Int. Journal of Computer Vision 61(1), 103–112 (2005)

    Article  Google Scholar 

  6. Hubert, L., Schultz, J.: Quadratic assignment as a general data-analysis strategy. British Journal of Mathematical and Statistical Psychology 29, 190–241 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  7. Kashima, H., Inokuchi, A.: Marginalized kernels between labeled graphs. In: Proc. 20th Int. Conference on Machine Learning, pp. 321–328 (2003)

    Google Scholar 

  8. Luo, B., Wilson, R.C., Hancock, E.R.: Spectral embedding of graphs. Pattern Recognition 36(10), 2213–2230 (2003)

    Article  MATH  Google Scholar 

  9. Nene, S.A., Nayar, S.K., Murase, H.: Columbia object image library (COIL-100). Tech. Rep. CUCS-006-96, Dep. of Computer Science, Columbia University (1996)

    Google Scholar 

  10. Neuhaus, M., Bunke, H.: Edit distance-based kernel functions for structural pattern classification. Pattern Recognition 39, 1852–1863 (2006)

    Article  MATH  Google Scholar 

  11. Riesen, K., Neuhaus, M., Bunke, H.: Graph embedding in vector spaces by means of prototype selection. In: Escolano, F., Vento, M. (eds.) GbRPR 2007. LNCS, vol. 4538, pp. 383–393. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  12. Tarr, M.J.: The object databank, http://www.cnbc.cmu.edu/tarrlab/stimuli/objects/index.html

  13. Torsello, A., Hancock, E.R.: Graph embedding using tree edit-union. Pattern Recognition 40, 1393–1405 (2007)

    Article  MATH  Google Scholar 

  14. Wilson, R.C., Hancock, E.R., Luo, B.: Pattern vectors from algebraic graph theory. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(7), 1112–1124 (2005)

    Article  Google Scholar 

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© 2010 Springer-Verlag Berlin Heidelberg

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Foggia, P., Vento, M. (2010). Graph Embedding for Pattern Recognition. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds) Recognizing Patterns in Signals, Speech, Images and Videos. ICPR 2010. Lecture Notes in Computer Science, vol 6388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17711-8_8

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  • DOI: https://doi.org/10.1007/978-3-642-17711-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17710-1

  • Online ISBN: 978-3-642-17711-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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