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Active-Learning Query Strategies Applied to Select a Graph Node Given a Graph Labelling

  • Conference paper
Graph-Based Representations in Pattern Recognition (GbRPR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7877))

Abstract

Given two graphs, the aim of graph matching is to find the “best” matching between nodes of one graph and nodes of the other graph. Due to distortions of the data and the complexity of the problem, in some applications, an active and interactive graph algorithm is needed. The active module queries one of the nodes of the graphs and the interactive module receives from an oracle (human or artificial) the node of the other graph that has to be mapped with and considers the new knowledge. We present different active strategies that decide the node to be queried and adapt these strategies to the graph-matching problem.

This research is supported by the CICYT project DPI 2010-17112.

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References

  1. Settles, B.: Active Learning Literature Survey. Computer Science Technical Report 1648, University of Wisconsin-Madison

    Google Scholar 

  2. Kotsiantis, S.B.: Supervised Machine Learning: A Review of Classification Techniques. Informatica 31, 249–268 (2007)

    MathSciNet  MATH  Google Scholar 

  3. Serratosa, F., Cortés, X., Solé-Ribalta, A.: Interactive Graph Matching by means of Imposing the Pairwise Costs. In: International Conference on Pattern Recognition, ICPR 2012, Accepted for publication, Tsukuba (2012)

    Google Scholar 

  4. Cortés, X., Serratosa, F., Solé-Ribalta, A.: Active Graph Matching based on Pairwise Probabilities between nodes. In: Gimel’farb, G., Hancock, E., Imiya, A., Kuijper, A., Kudo, M., Omachi, S., Windeatt, T., Yamada, K. (eds.) SSPR&SPR 2012. LNCS, vol. 7626, pp. 98–106. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  5. Fekete, G., Eklundh, J.O., Rosenfeld, A.: Relaxation: Evaluation and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 3(4), 459–469 (1981)

    Article  Google Scholar 

  6. Gold, S., Rangarajan, A.: A Graduated Assignment Algorithm for Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(4), 377–388 (1996)

    Article  Google Scholar 

  7. Luo, B., Hancock, E.R.: Structural graph matching using the EM algorithm and singular value decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(10), 1120–1136 (2001)

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Sanromà, G., Alquézar, R., Serratosa, F.: A New Graph Matching Method for Point-Set Correspondence using the EM Algorithm and Softassign. Computer Vision and Image Understanding 116(2), 292–304 (2012)

    Article  Google Scholar 

  10. Sanromà, G., Alquézar, R., Serratosa, F., Herrera, B.: Smooth Point-set Registration using Neighbouring Constraints. Pattern Recognition Letters, PRL 33, 2029–2037 (2012)

    Article  Google Scholar 

  11. Settles, B., Craven, M.: An analysis of active learning strategies for sequence labelling tasks. In: Conference on Empirical Methods in Natural Language Processing, pp. 1069–1078 (2008)

    Google Scholar 

  12. Culotta, A., McCallum, A.: Reducing labelling effort for structured prediction tasks. In: National Conference on Artificial Intelligence, pp. 746–751 (2005)

    Google Scholar 

  13. Melville, P., Mooney, R.: Diverse ensembles for active learning. In: International Conference on Machine Learning, pp. 584–591 (2004)

    Google Scholar 

  14. Settles, B., Craven, M., Ray, S.: Multiple-instance active learning. Advances in Neural Information Processing Systems 20, 1289–1296 (2008)

    Google Scholar 

  15. Zhang, T., Oles, F.J.: A probability analysis on the value of unlabelled data for classification problems. In: International Conference on Machine Learning, pp. 1191–1198 (2000)

    Google Scholar 

  16. Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: International Conference on Machine Learning, pp. 441–448 (2001)

    Google Scholar 

  17. Sanfeliu, A., Fu, K.: A distance measure between attributed relational graphs for pattern recognition. IEEE Trans. on Sys. Man and Cybern 13, 353–362 (1983)

    Article  MATH  Google Scholar 

  18. Riesen, K., Bunke, H.: IAM graph database repository for graph based pattern recognition and machine learning. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) SSPR & SPR 2008. LNCS, vol. 5342, pp. 287–297. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  19. Serratosa, F., Cortés, X., Solé-Ribalta, A.: Component Retrieval based on a Database of Graphs for Hand-Written Electronic-Scheme Digitalisation. Expert Systems With Applications, ESWA 40, 2493–2502 (2013)

    Article  Google Scholar 

  20. Caetano, T.S., Caelli, T., Schuurmans, D., Barone, D.A.C.: Graphical Models and Point Pattern Matching. IEEE Trans. Pattern Analysis and Machine Intelligence 28(10), 1646–1663 (2006)

    Article  Google Scholar 

  21. Solé, A., Serratosa, F., Sanfeliu, A.: On the Graph Edit Distance cost: Properties and Applications. International Journal of Pattern Recognition and Artificial Intelligence, IJPRAI 26(5) (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Spain

    Xavier Cortés & Francesc Serratosa

Authors
  1. Xavier Cortés
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  2. Francesc Serratosa
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Editor information

Editors and Affiliations

  1. Dept. of Pattern Recognition and Image Processing, Vienna University of Technology, Favoritenstr. 9-11, 186-3, 1040, Vienna, Austria

    Walter G. Kropatsch , Nicole M. Artner  & Yll Haxhimusa ,  & 

  2. Department of Computer Science, University of Münster, Einsteinstr. 62, 48149, Münster, Germany

    Xiaoyi Jiang

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Cortés, X., Serratosa, F. (2013). Active-Learning Query Strategies Applied to Select a Graph Node Given a Graph Labelling. In: Kropatsch, W.G., Artner, N.M., Haxhimusa, Y., Jiang, X. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2013. Lecture Notes in Computer Science, vol 7877. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38221-5_7

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  • DOI: https://doi.org/10.1007/978-3-642-38221-5_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38220-8

  • Online ISBN: 978-3-642-38221-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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