An Interactive Model for Structural Pattern Recognition based on the Bayes Classifier

Xavier Cortés, Francesc Serratosa, Carlos Francisco Moreno-García

2015

Abstract

This paper presents an interactive model for structural pattern recognition based on a naïve Bayes classifier. In some applications, the automatically computed correlation between local parts of two images is not good enough. Moreover, humans are very good at locating and mapping local parts of images although any kind of global transformations had been applied to these images. In our model, the user interacts on the automatically obtained correlation (or correspondences between local parts) and helps the system to find the best correspondence while the global transformation parameters are automatically recomputed. The model is based on a Bayes classifier in which the human interaction is properly modelled and embedded in the model. We show that with little human interaction, the quality of the returned correspondences and global transformation parameters drastically increases.

References

  1. Davide Maltoni, Dario Maio, Anil K. Jain & Salil Prabhakar, “Handbook of Fingerprint Recognition”, Springer, (2009).
  2. Salim Jouili & Salvatore Tabbone, “Hypergraph-based image retrieval for graph-based representation”, Pattern Recognition, Available online 28 April (2012).
  3. Justine Lebrun, Philippe-Henri Gosselin & Sylvie PhilippFoliguet, “Inexact graph matching based on kernels for object retrieval in image databases”, Image and Vision Computing, 29 (11), pp: 716-729, (2011).
  4. In Kyu Park, Il Dong Yun & Sang Uk Lee, “Colour image retrieval using hybrid graph representation”, Image and Vision Computing, 17 (7), pp: 465-474, (1999).
  5. Toselli, A. H., Vidal E. & Casacuberta, F. “Multimodal interactive pattern recognition and applications”, Springer (2011).
  6. A. Solé & F. Serratosa, Models and Algorithms for computing the Common Labelling of a set of Attributed Graphs, Computer Vision and Image Understanding 115 (7), pp: 929-945, 2011.
  7. G. Sanromà, R. Alquézar, F. Serratosa & B. Herrera, Smooth Point-set Registration using Neighbouring Constraints, Pattern Recognition Letters 33, pp: 2029- 2037, 2012.
  8. F. Serratosa, X. Cortés & A. Solé, Component Retrieval based on a Database of Graphs for Hand-Written Electronic-Scheme Digitalisation, Expert Systems With Applications 40, pp: 2493 -2502, 2013.
  9. A. Solé & F. Serratosa, Graduated Assignment Algorithm for Multiple Graph Matching based on a Common Labelling, International Journal of Pattern Recognition and Artificial Intelligence 27 (1), pp: 1 - 27 2013.
  10. Jarvis, R. A. “An interactive minicomputer laboratory for graphics, image processing and pattern recognition”, Computer 7 (10), pp: 49-60, (1974).
  11. G. H Landeweerd, E. S Gelsema, M Bins, M. R Halie, “Interactive pattern recognition of blood cells in malignant lymphomas”, Pattern Recognition, 14, (1- 6), pp: 239-244, (1981).
  12. Alberto Sanchís, Alfons Juan, Enrique Vidal: “A WordBased Naïve Bayes Classifier for Confidence Estimation in Speech Recognition”. IEEE Transactions on Audio, Speech & Language Processing 20(2): 565-574, (2012).
  13. F. Serratosa, R. Alquézar & N. Amézquita, A Probabilistic Integrated Object Recognition and Tracking Framework, Expert Systems With Applications 39, pp: 7302-7318, 2012.
  14. Jie Zou, George Nagy, “Visible models for interactive pattern recognition”, Pattern Recognition Letters, 28, (16), pp: 2335-2342, (2007).
  15. Harris, C. & Stephens, M. “A combined corner and edge detector”, Proceedings of the 4th Alvey Vision Conference. pp. 147-151, (1988).
  16. Lowe, D. G., “Object recognition from local scaleinvariant features”. Proceedings of the International Conference on Computer Vision. 2. pp. 1150-1157, (1999).
  17. Mikolajczyk, K. & Schmid, C. “A performance evaluation of local descriptors”. IEEE Transactions on Pattern Analysis & Machine Intelligence, 27(10): 1615-1630 (2005).
  18. Sebastian, T., Klein, P. & Kimia, B. “Recognition of shapes by editing their shock graphs”. IEEE Trans. on Pattern Analysis & Machine Intelligence 26(5): 550- 571 (2004).
  19. Sanfeliu, A. & Fu, K. “A distance measure between attributed relational graphs for pattern recognition”. IEEE Transactions on Systems, Man and Cybernetics 13, 353-362. (1983).
  20. Zhabg, Z. “Iterative Point Matching for Registration of Free-form Curves”, International Journal of Computer Vision 13(2): 119-152. (1992).
  21. Rangarajan, A., H. Chui, et al. The softassign procrustes matching algorithm. International Conference on Information Processing in Medical Imaging. (1997).
  22. Andrew D. J. Cross, Edwin R. Hancock: “Graph Matching With a Dual-Step EM Algorithm” IEEE Trans. Pattern Analysis & Machine Intellig., 20(11): 1236-1253 (1998).
  23. Aguilar, W., Frauel, Y., Escolano, F. & Martinez-Perez, M. E. “A robust graph transformation matching for non-rigid registration”. Image and Vision Computing 27, 897-910. (2009).
  24. G. Sanromà, R. Alquézar, & F. Serratosa, “A New Graph Matching Method for Point-Set Correspondence using the EM Algorithm and Softassign”, Computer Vision and Image Understanding CVIU, 116(2), pp: 292-304, (2012).
  25. Fischler, M. and R. Bolles “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”, Communications of the ACM 24(6): 381-395 (1981).
  26. F. Serratosa, “Fast Computation of Bipartite Graph Matching”, Pattern Recognition Letters, PRL 45, pp: 244-250, 2014.
  27. A. Solé, F. Serratosa & A. Sanfeliu, “On the Graph Edit Distance cost: Properties and Applications”, International Journal of Pattern Recognition and Artificial Intelligence, IJPRAI 26, (5), pp:, 2012.
  28. X. Cortés & F. Serratosa, “An Interactive Method for the Image Alignment problem based on Partially Supervised Correspondence”, Expert Systems With Applications, 42 (1), pp: 179 - 192, 2015.
  29. Richard O. Duda & Peter E. Hart, “Pattern Classification and Scene Analysis”, John Wiley, 1995.
  30. CMU “house” data set, http://vasc.ri.cmu.edu /idb/html/motion/house/index.html, 2009.
  31. T. S. Caetano, T. Caelli, D. Schuurmans & D. A. C. Barone, “Graphical Models and Point Pattern Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1646-1663, (2006).
  32. Munkres, J. “Algorithms for the assignment and transportation problems”. Journal of the Society for Industrial and Applied Mathematics 5(1), 32-38, (1957).
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Paper Citation


in Harvard Style

Cortés X., Serratosa F. and Moreno-García C. (2015). An Interactive Model for Structural Pattern Recognition based on the Bayes Classifier . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-076-5, pages 240-247. DOI: 10.5220/0005201602400247


in Bibtex Style

@conference{icpram15,
author={Xavier Cortés and Francesc Serratosa and Carlos Francisco Moreno-García},
title={An Interactive Model for Structural Pattern Recognition based on the Bayes Classifier},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2015},
pages={240-247},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005201602400247},
isbn={978-989-758-076-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - An Interactive Model for Structural Pattern Recognition based on the Bayes Classifier
SN - 978-989-758-076-5
AU - Cortés X.
AU - Serratosa F.
AU - Moreno-García C.
PY - 2015
SP - 240
EP - 247
DO - 10.5220/0005201602400247