Publication detail

Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion

ŠTRBA, M. HEROUT, A. HAVEL, J.

Original Title

Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

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.

Keywords

Digit Recognition, Classifier Fusion, Multiresolution

Authors

ŠTRBA, M.; HEROUT, A.; HAVEL, J.

RIV year

2011

Released

1. 6. 2011

Publisher

Springer Verlag

Location

Berlin

ISBN

978-3-642-21256-7

Book

Proceedings of IbPRIA 2011, LNCS

Pages from

726

Pages to

733

Pages count

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"
}