LOSS-WEIGHTED DECODING FOR ERROR-CORRECTING OUTPUT CODIN

Sergio Escalera, Oriol Pujol, Petia Radeva

Abstract

The multi-class classification is a challenging problem for several applications in Computer Vision. Error Correcting Output Codes technique (ECOC) represents a general framework capable to extend any binary classification process to the multi-class case. In this work, we present a novel decoding strategy that takes advantage of the ECOC coding to outperform the up to now existing decoding strategies. The novel decoding strategy is applied to the state-of-the-art coding designs, extensively tested on the UCI Machine Learning repository database and in two real vision applications: tissue characterization in medical images and traffic sign categorization. The results show that the presented methodology considerably increases the performance of the traditional ECOC strategies and the state-of-the-art multi-classifiers.

References

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Paper Citation


in Harvard Style

Escalera S., Pujol O. and Radeva P. (2008). LOSS-WEIGHTED DECODING FOR ERROR-CORRECTING OUTPUT CODIN . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 117-122. DOI: 10.5220/0001071601170122


in Bibtex Style

@conference{visapp08,
author={Sergio Escalera and Oriol Pujol and Petia Radeva},
title={LOSS-WEIGHTED DECODING FOR ERROR-CORRECTING OUTPUT CODIN},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={117-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001071601170122},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - LOSS-WEIGHTED DECODING FOR ERROR-CORRECTING OUTPUT CODIN
SN - 978-989-8111-21-0
AU - Escalera S.
AU - Pujol O.
AU - Radeva P.
PY - 2008
SP - 117
EP - 122
DO - 10.5220/0001071601170122