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Francesco Ciompi; Oriol Pujol; Petia Radeva |
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Title |
ECOC-DRF: Discriminative random fields based on error correcting output codes |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
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PR |
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47 |
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6 |
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2193-2204 |
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Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models |
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We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments. |
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LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 |
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Admin @ si @ CPR2014b |
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2470 |
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Author |
Estefania Talavera; Carolin Wuerich; Nicolai Petkov; Petia Radeva |
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Title |
Topic modelling for routine discovery from egocentric photo-streams |
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Journal Article |
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2020 |
Publication |
Pattern Recognition |
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PR |
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104 |
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Pages |
107330 |
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Routine; Egocentric vision; Lifestyle; Behaviour analysis; Topic modelling |
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Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals’ lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed. |
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MILAB; no proj |
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Admin @ si @ TWP2020 |
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3435 |
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Cristina Cañero; Petia Radeva |
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Title |
Vesselness enhancement diffusion |
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Year |
2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
Issue |
16 |
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3141–3151 |
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Abstract |
IF: 0.809 |
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MILAB |
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BCNPCL @ bcnpcl @ CaR2003 |
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371 |
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Jaume Amores; Petia Radeva |
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Registration and Retrieval of Highly Elastic Bodies using Contextual Information |
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2005 |
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Pattern Recognition Letters |
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26 |
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11 |
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1720–1731 |
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IF: 1.138 |
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ADAS;MILAB |
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ADAS @ adas @ AmR2005b |
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592 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
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Journal Article |
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Year |
2006 |
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Pattern Recognition Letters |
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PRL |
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27 |
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3 |
Pages |
201–209 |
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ADAS;MILAB |
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ADAS @ adas @ ASR2006 |
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643 |
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