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

English Title

Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion

Type

conference paper

Language

en

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.

English 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

RIV year

2011

Released

01.06.2011

Publisher

Springer Verlag

Location

Berlin

ISBN

978-3-642-21256-7

Book

Proceedings of IbPRIA 2011, LNCS

Edition

NEUVEDEN

Edition number

NEUVEDEN

Pages from

726

Pages to

733

Pages count

8

Documents

BibTex 


@inproceedings{BUT76284,
  author="Miroslav {Štrba} and Adam {Herout} and Jiří {Havel}",
  title="Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion",
  annote="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.",
  address="Springer Verlag",
  booktitle="Proceedings of IbPRIA 2011, LNCS",
  chapter="76284",
  edition="NEUVEDEN",
  howpublished="print",
  institution="Springer Verlag",
  year="2011",
  month="june",
  pages="726--733",
  publisher="Springer Verlag",
  type="conference paper"
}