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A Graph-Kernel Method for Re-identification

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Book cover Image Analysis and Recognition (ICIAR 2011)

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Abstract

Re-identification, that is recognizing that an object appearing in a scene is a reoccurrence of an object seen previously by the system (by the same camera or possibly by a different one) is a challenging problem in video surveillance. In this paper, the problem is addressed using a structural, graph-based representation of the objects of interest. A recently proposed graph kernel is adopted for extending to this representation the Principal Component Analyisis (PCA) technique. An experimental evaluation of the method has been performed on two video sequences from the publicly available PETS2009 database.

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References

  1. http://www.compuphase.com/cmetric.htm

  2. Database: Pets 2009 (2009), http://www.cvg.rdg.ac.uk/PETS2009/

  3. Bak, S., Corvee, E., Brmond, F., Thonnat, M.: Person re-identification using haar-based and dcd-based signature. In: 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (2010)

    Google Scholar 

  4. Bazzani, L., Cristani, M., Perina, A., Farenzena, M., Murino, V.: Multiple-shot person re-identification by hpe signature. In: Proceedings of 20th International Conference on Pattern Recognition, ICPR 2010 (2010)

    Google Scholar 

  5. Bird, N., Masoud, O., Papanikolopoulos, N., Isaacs, A.: Detection of loitering individuals in public transportation areas. IEEE Transactions on Intelligent Transportation Systems 6(2), 167–177 (2005)

    Article  Google Scholar 

  6. Brun, L., Conte, D., Foggia, P., Vento, M., Villemin, D.: Symbolic learning vs. graph kernels: An experimental comparison in a chemical application. In: 14th Conf. on Advances in Databases and Information Systems (ADBIS) (2010)

    Google Scholar 

  7. Conte, D., Foggia, P., Percannella, G., Vento, M.: Performance evaluation of a people tracking system on pets2009 database. In: Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (2010)

    Google Scholar 

  8. Desobry, F., Davy, M., Doncarli, C.: An online kernel change detection algorithm. IEEE Transaction on Signal Processing 53(8), 2961–2974 (2005)

    Article  MathSciNet  Google Scholar 

  9. Dupé, F.X., Brun, L.: Tree covering within a graph kernel framework for shape classification. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 278–287. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  10. Gandhi, T., Trivedi, M.M.: Panoramic appearance map (pam) for multi-camera based person re-identification. In: IEEE International Conference on Video and Signal Based Surveillance, AVSS 2006 (2006)

    Google Scholar 

  11. Gauzere, B., Brun, L., Villemin, D.: Graph edit distance and treelet kernels for chemoinformatic. In: Graph Based Representation 2011, IAPR-TC15, Munster, Germany (May 2011) (submitted)

    Google Scholar 

  12. Haussler, D.: Convolution kernels on discrete structures. Tech. rep., Department of Computer Science, University of California at Santa Cruz (1999)

    Google Scholar 

  13. Hoffmann, H.: Kernel pca for novelty detection. Pattern Recognition 40(3), 863 (2007)

    Article  MATH  Google Scholar 

  14. Kashima, H., Tsuda, K., Inokuchi, A.: Marginalized kernel between labeled graphs. In: Proc. of the Twentieth International conference on Machine Learning (2003)

    Google Scholar 

  15. Mah, P., Vert, J.P.: Graph kernels based on tree patterns for molecules. Machine Learning 75(1), 3–35 (2008)

    Article  Google Scholar 

  16. Neuhaus, M., Bunke, H.: Bridging the Gap Between Graph Edit Distance and Kernel Machines. World Scientific Publishing Co., Inc., River Edge (2007)

    Book  MATH  Google Scholar 

  17. Nock, R., Nielsen, F.: Statistical region merging. IEEE Transaction on Pattern Analysis and Machine Intelligence 26(11), 1452–1458 (2004)

    Article  Google Scholar 

  18. de Oliveira, I.O., de Souza Pio, J.L.: People reidentification in a camera network. In: IEEE Int. Conf. on Dependable, Autonomic and Secure Computing (2009)

    Google Scholar 

  19. Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vision Computing 27(7), 950–959 (2009)

    Article  Google Scholar 

  20. Riesen, K., Neuhaus, M., Bunke, H.: Bipartite graph matching for computing the edit distance of graphs. In: Escolano, F., Vento, M. (eds.) GbRPR. LNCS, vol. 4538, pp. 1–12. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  21. Scholkopf, B., Platt, J., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation 13, 1443–1471 (2001)

    Article  MATH  Google Scholar 

  22. Shervashidze, N., Vishwanathan, S.V., Petri, T.H., Mehlhorn, K., Borgwardt, K.M.: Efficient graphlet kernels for large graph comparison. In: Twelfth International Conference on Artificial Intelligence and Statistics (2009)

    Google Scholar 

  23. Shervashidze, N., Borgwardt, K.: Fast subtree kernels on graphs. In: Advances in Neural Information Processing Systems 22. Curran Associates Inc. (2009)

    Google Scholar 

  24. Steinke, F., Schökopf, B.: Kernels, regularization and differential equations. Pattern Recognition 41(11), 3271–3286 (2008)

    Article  MATH  Google Scholar 

  25. Tax, D., Duin, R.: Support vector domain description. Pattern Recognition Letters 20, 1191–1199 (1999)

    Article  Google Scholar 

  26. TruongCong, D.N., Khoudour, L., Achard, C., Meurie, C., Lezoray, O.: People re-identification by spectral classification of silhouettes. Signal Processing 90, 2362–2374 (2010)

    Article  MATH  Google Scholar 

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Brun, L., Conte, D., Foggia, P., Vento, M. (2011). A Graph-Kernel Method for Re-identification. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_18

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  • DOI: https://doi.org/10.1007/978-3-642-21593-3_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21592-6

  • Online ISBN: 978-3-642-21593-3

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