Detail publikace

Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion

ŠTRBA, M. HEROUT, A. HAVEL, J.

Originální název

Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

One common approach to construction of highly accurate classifiers for hadwritten digit recognition is fusion of several weaker classifiers into a compound one, which (when meeting some constraints) outperforms all the individual fused classifiers.  This paper studies the possibility of fusing classifiers of different kinds (Self-Organizing Maps, Randomized Trees, and AdaBoost with MB-LBP weak hypotheses) constructed on training sets resampled to different resolutions.  While it is common to select one resolution of the input samples as the ``ideal one'' and fuse classifiers constructed for it, this paper shows that the accuracy of classification can be improved by fusing information from several scales.

Klíčová slova

Digit Recognition, Classifier Fusion, Multiresolution

Autoři

ŠTRBA, M.; HEROUT, A.; HAVEL, J.

Rok RIV

2011

Vydáno

1. 6. 2011

Nakladatel

Springer Verlag

Místo

Berlin

ISBN

978-3-642-21256-7

Kniha

Proceedings of IbPRIA 2011, LNCS

Strany od

726

Strany do

733

Strany počet

8

BibTex

@inproceedings{BUT76284,
  author="Miroslav {Štrba} and Adam {Herout} and Jiří {Havel}",
  title="Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion",
  booktitle="Proceedings of IbPRIA 2011, LNCS",
  year="2011",
  pages="726--733",
  publisher="Springer Verlag",
  address="Berlin",
  isbn="978-3-642-21256-7"
}