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

Xavier Cortés, Francesc Serratosa, Carlos Francisco Moreno-García


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.


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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

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,},

in EndNote Style

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