| Records |
| Author |
Eloi Puertas; Sergio Escalera; Oriol Pujol |
| Title |
Classifying Objects at Different Sizes with Multi-Scale Stacked Sequential Learning |
Type |
Conference Article |
| Year |
2010 |
Publication |
13th International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
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| Volume |
220 |
Issue |
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Pages |
193–200 |
| Keywords |
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| Abstract |
Sequential learning is that discipline of machine learning that deals with dependent data. In this paper, we use the Multi-scale Stacked Sequential Learning approach (MSSL) to solve the task of pixel-wise classification based on contextual information. The main contribution of this work is a shifting technique applied during the testing phase that makes possible, thanks to template images, to classify objects at different sizes. The results show that the proposed method robustly classifies such objects capturing their spatial relationships. |
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Thesis |
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Place of Publication |
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Editor |
R. Alquezar, A. Moreno, J. Aguilar |
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Edition |
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ISBN |
978-1-60750-642-3 |
Medium |
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Expedition |
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Conference |
CCIA |
| Notes |
HUPBA;MILAB |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ PEP2010 |
Serial |
1448 |
| Permanent link to this record |
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| Author |
Eloi Puertas; Sergio Escalera; Oriol Pujol |
| Title |
Multi-Class Multi-Scale Stacked Sequential Learning |
Type |
Conference Article |
| Year |
2011 |
Publication |
10th International Conference on Multiple Classifier Systems |
Abbreviated Journal |
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| Volume |
6713 |
Issue |
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Pages |
197-206 |
| Keywords |
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| Abstract |
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| Address |
Napoles, Italy |
| Corporate Author |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
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Editor |
Carlo Sansone; Josef Kittler; Fabio Roli |
| Language |
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Summary Language |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
MCS |
| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ PEP2011b |
Serial |
1772 |
| Permanent link to this record |
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| Author |
Eloi Puertas; Sergio Escalera; Oriol Pujol |
| Title |
Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification |
Type |
Journal Article |
| Year |
2015 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
| Volume |
18 |
Issue |
2 |
Pages |
247-261 |
| Keywords |
Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification |
| Abstract |
In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches. |
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Springer-Verlag |
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Edition |
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| ISSN |
1433-7541 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ PEP2013 |
Serial |
2251 |
| Permanent link to this record |
