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Bogdan Raducanu; Jordi Vitria |
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Face Recognition by Artificial Vision Systems: A Cognitive Perspective |
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2008 |
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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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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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2013 |
Publication |
International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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27 |
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5 |
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1350014-1350035 |
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Keywords |
One-class Classifier; Ensemble Methods; Bagging and Outliers |
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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 |
Bogdan Raducanu; Jordi Vitria; Ales Leonardis |
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Title |
Online pattern recognition and machine learning techniques for computer-vision: Theory and applications |
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2010 |
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Image and Vision Computing |
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IMAVIS |
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28 |
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7 |
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1063–1064 |
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(Editorial for the Special Issue on Online pattern recognition and machine learning techniques)
In real life, visual learning is supposed to be a continuous process. This paradigm has found its way also in artificial vision systems. There is an increasing trend in pattern recognition represented by online learning approaches, which aims at continuously updating the data representation when new information arrives. Starting with a minimal dataset, the initial knowledge is expanded by incorporating incoming instances, which may have not been previously available or foreseen at the system’s design stage. An interesting characteristic of this strategy is that the train and test phases take place simultaneously. Given the increasing interest in this subject, the aim of this special issue is to be a landmark event in the development of online learning techniques and their applications with the hope that it will capture the interest of a wider audience and will attract even more researchers. We received 19 contributions, of which 9 have been accepted for publication, after having been subjected to usual peer review process. |
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Elsevier |
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0262-8856 |
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OR;MV |
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BCNPCL @ bcnpcl @ RVL2010 |
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1280 |
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Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Title |
Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes |
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2013 |
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Komputer Sapiens |
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KS |
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1 |
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20-25 |
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Admin @ si @ TSR2013 |
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2231 |
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Bogdan Raducanu; D. Gatica-Perez |
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Title |
Inferring competitive role patterns in reality TV show through nonverbal analysis |
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2012 |
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Multimedia Tools and Applications |
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MTAP |
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56 |
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1 |
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207-226 |
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This paper introduces a new facet of social media, namely that depicting social interaction. More concretely, we address this problem from the perspective of nonverbal behavior-based analysis of competitive meetings. For our study, we made use of “The Apprentice” reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status, and predicting the fired candidates. We address this problem by adopting both supervised and unsupervised strategies. The current study was carried out using nonverbal audio cues. Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. The analysis is based on two types of data: individual and relational measures. Results obtained from the analysis of a full season of the show are promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach has been conveniently compared with the Influence Model, demonstrating its superiority. |
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Elsevier |
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1380-7501 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaG2012 |
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1360 |
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