Resumen
In order to increase the performance in the handwritten digit recognition field, researchers commonly combine a variety of features to represent a pattern. This approach has showed to be very effective in practice. The classical approach to combine features is by concatenating the underlying feature vectors. A drawback of this approach is that it could generate high-dimensional descriptors, which increases the complexity of the training process. Instead, we propose to use a pooling based classifier, that allow us to get not only a faster training process but also outperforming results. For evaluation, we used two state-of-the-art handwritten digit datasets: CVL and MNIST. In addition, we show that a simple rectangular spatial division, that characterize our descriptors, yields competitive results and a smaller computation cost with respect to other more complex zoning techniques.
Idioma original | Inglés |
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Título de la publicación alojada | Progress in Pattern Recognition Image Analysis, Computer Vision and Applications - 19th Iberoamerican Congress, CIARP 2014, Proceedings |
Editores | Eduardo Bayro-Corrochano, Edwin Hancock |
Editorial | Springer Verlag |
Páginas | 658-665 |
Número de páginas | 8 |
ISBN (versión digital) | 9783319125671 |
DOI | |
Estado | Publicada - 2014 |
Publicado de forma externa | Sí |
Evento | 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 - Puerto Vallarta, México Duración: 2 nov. 2014 → 5 nov. 2014 |
Serie de la publicación
Nombre | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volumen | 8827 |
ISSN (versión impresa) | 0302-9743 |
ISSN (versión digital) | 1611-3349 |
Conferencia
Conferencia | 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 |
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País/Territorio | México |
Ciudad | Puerto Vallarta |
Período | 2/11/14 → 5/11/14 |
Nota bibliográfica
Publisher Copyright:© Springer International Publishing Switzerland 2014.