F. Calle-Alonso, J. P. Arias-Nicolás, C. J. Pérez, J. Martín


In this work, a novel classification method is proposed. The method uses a Bayesian regression model in a pairwise comparison framework. As a result, we obtain an automatic classification tool that allows new cases to be classified without the interaction of the user. The differences with other classification methods, are the two innovative relevance feedback tools for an iterative classification process. The first one is the information obtained from user after validating the results of the automatic classification. The second difference is the continuous adaptive distribution of the model’s parameters. It also has the advantage that can be used with problems with both a large number of characteristics and few number of elements. The method could be specially helpful for those professionals who have to make a decision based on images classification, such as doctors to determine the diagnosis of patients, meteorologists, traffic police to detect license plate, etc.


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Paper Citation

in Harvard Style

Calle-Alonso F., P. Arias-Nicolás J., J. Pérez C. and Martín J. (2012). BAYESIAN SUPERVISED IMAGE CLASSIFICATION BASED ON A PAIRWISE COMPARISON METHOD . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: IATMLRP, (ICPRAM 2012) ISBN 978-989-8425-98-0, pages 467-473. DOI: 10.5220/0003876004670473

in Bibtex Style

author={F. Calle-Alonso and J. P. Arias-Nicolás and C. J. Pérez and J. Martín},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: IATMLRP, (ICPRAM 2012)},

in EndNote Style

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: IATMLRP, (ICPRAM 2012)
SN - 978-989-8425-98-0
AU - Calle-Alonso F.
AU - P. Arias-Nicolás J.
AU - J. Pérez C.
AU - Martín J.
PY - 2012
SP - 467
EP - 473
DO - 10.5220/0003876004670473