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F. Pla; Petia Radeva; Jordi Vitria |
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Non-parametric distance-based classification techniques and their applications |
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2008 |
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Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications |
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11 |
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3-4 |
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223–225 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ PRV2008 |
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999 |
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Author |
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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OR;MV |
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BCNPCL @ bcnpcl @ RaV2008b |
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1007 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
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Title |
3D Face Pose Detection and Tracking Using Monocular Videos: Tool and Application |
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2008 |
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IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
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OR;MV |
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BCNPCL @ bcnpcl @ DoR2008d |
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1109 |
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Author |
Xavier Baro; Sergio Escalera; Jordi Vitria; Oriol Pujol; Petia Radeva |
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Title |
Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification |
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2009 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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10 |
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1 |
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113–126 |
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The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination. |
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1524-9050 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ BEV2008 |
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1116 |
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Author |
David Masip; Agata Lapedriza; Jordi Vitria |
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Title |
Boosted Online Learning for Face Recognition |
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2009 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics part B |
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TSMCB |
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39 |
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2 |
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530–538 |
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Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight. |
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1083–4419 |
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OR;MV |
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BCNPCL @ bcnpcl @ MLV2009 |
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1155 |
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