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Jorge Bernal; Aymeric Histace; Marc Masana; Quentin Angermann; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Maroua Hammami; Ana Garcia Rodriguez; Henry Cordova; Olivier Romain; Gloria Fernandez Esparrach; Xavier Dray; F. Javier Sanchez |
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Title |
GTCreator: a flexible annotation tool for image-based datasets |
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Journal Article |
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Year |
2019 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCAR |
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14 |
Issue |
2 |
Pages |
191–201 |
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Annotation tool; Validation Framework; Benchmark; Colonoscopy; Evaluation |
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Abstract Purpose: Methodology evaluation for decision support systems for health is a time consuming-task. To assess performance of polyp detection
methods in colonoscopy videos, clinicians have to deal with the annotation
of thousands of images. Current existing tools could be improved in terms of
exibility and ease of use. Methods:We introduce GTCreator, a exible annotation tool for providing image and text annotations to image-based datasets.
It keeps the main basic functionalities of other similar tools while extending
other capabilities such as allowing multiple annotators to work simultaneously
on the same task or enhanced dataset browsing and easy annotation transfer aiming to speed up annotation processes in large datasets. Results: The
comparison with other similar tools shows that GTCreator allows to obtain
fast and precise annotation of image datasets, being the only one which offers
full annotation editing and browsing capabilites. Conclusions: Our proposed
annotation tool has been proven to be efficient for large image dataset annota-
tion, as well as showing potential of use in other stages of method evaluation
such as experimental setup or results analysis. |
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MV; 600.096; 600.109; 600.119; 601.305 |
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Admin @ si @ BHM2019 |
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3163 |
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Author |
David Masip; Jordi Vitria |
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Title |
Feature Extraction for Nearest Neighbor Classification. Application to Gender Recognition |
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2005 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
International Journal of Intelligent Systems, 20(5): 561–576 (IF: 0.657) |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV2005 |
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562 |
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Author |
Bogdan Raducanu; Jordi Vitria |
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Title |
Face Recognition by Artificial Vision Systems: A Cognitive Perspective |
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2008 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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22 |
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5 |
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899–913 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaV2008b |
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1007 |
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Author |
Santiago Segui; Laura Igual; Jordi Vitria |
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Title |
Bagged One Class Classifiers in the Presence of Outliers |
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Journal Article |
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2013 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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27 |
Issue |
5 |
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1350014-1350035 |
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Keywords |
One-class Classifier; Ensemble Methods; Bagging and Outliers |
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Abstract |
The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers.
However, their application to one-class classification has been rather limited. In
this paper, we present a new ensemble method based on a non-parametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the non-parametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way. |
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OR; 600.046;MV |
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Admin @ si @ SIV2013 |
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2256 |
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Author |
Petia Radeva; Jordi Vitria |
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Title |
Corkinspect: Statistical Learning of Natural Material |
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Journal |
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2004 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Italian Beverage Technology, 13(38):11–18 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RaV2004b |
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514 |
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