Prof. Ing. Adam Herout, Ph.D.

ŠTRBA Miroslav, HEROUT Adam and HAVEL Jiří. Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion. In: Proceedings of IbPRIA 2011, LNCS. Berlin: Springer Verlag, 2011, pp. 726-733. ISBN 978-3-642-21256-7.
Publication language:english
Original title:Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion
Title (cs):Rozpoznávání ručně psaných číslic vylepšeno fúzí klasifikátorů v různých rozlišeních
Proceedings:Proceedings of IbPRIA 2011, LNCS
Conference:Iberian Conference on Pattern Recognition and Image Analysis
Place:Berlin, DE
Publisher:Springer Verlag
Digit Recognition, Classifier Fusion, Multiresolution
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.
   author = {Miroslav {\v{S}}trba and Adam Herout and
	Ji{\v{r}}{\'{i}} Havel},
   title = {Handwritten Digits Recognition Improved by
	Multiresolution Classifier Fusion},
   pages = {726--733},
   booktitle = {Proceedings of IbPRIA 2011, LNCS},
   year = 2011,
   location = {Berlin, DE},
   publisher = {Springer Verlag},
   ISBN = {978-3-642-21256-7},
   language = {english},
   url = {}

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