Název:	A Handcrafted Feature Descriptor for Word Recognition using Embedded Prototype Subspace Classifiers
Autoři:	Hast, Anders
Citace zdrojového dokumentu:	Journal of WSCG. 2022, vol. 30, no. 1-2, p. 82-90.
Datum vydání:	2022
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	článek article
URI:	http://hdl.handle.net/11025/49397
ISSN:	1213-6972 (print) 1213-6964 (on-line)
Klíčová slova:	diskrétní Fourierova transformace;Gaborovy filtry;podprostory;vestavěné prototypy;shlukování;F-skóre;variabilita;hluboké učení;t-SNE
Klíčová slova v dalším jazyce:	discrete Fourier transform;Gabor filters;subspaces;embedded prototypes;clustering;F1 score;variability;deep learning;t-SNE
Abstrakt v dalším jazyce:	The purpose of this paper is to in detail describe and analyse a Fourier based handcrafted descriptor for word recognition. Especially, it is discussed how the Variability in the results can be analysed and visualised. This efficiency of the descriptor is evaluated for the use with embedded prototype subspace classifiers for handwritten word recognition. Nonetheless, it can be used with any classifier for any purpose. An hierarchical composition of discrete semicircles in the Fourier-space is proposed and it will will be show how this compares to Gabor filters, which can be used to extract edges in an image. In comparison to Histogram of Oriented Gradients, the proposed feature descriptor performs better in this scenario. Compression using PCA turns out to be able to increase both the F1-score as well as decreasing the Variability.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	Volume 30, Number 1-2 (2021)

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