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Author David Masip; Agata Lapedriza; Jordi Vitria edit  doi
openurl 
  Title Boosted Online Learning for Face Recognition Type Journal Article
  Year 2009 Publication IEEE Transactions on Systems, Man and Cybernetics part B Abbreviated Journal TSMCB  
  Volume 39 Issue 2 Pages (up) 530–538  
  Keywords  
  Abstract 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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  Series Volume Series Issue Edition  
  ISSN 1083–4419 ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ MLV2009 Serial 1155  
Permanent link to this record
 

 
Author Cesar Isaza; Joaquin Salas; Bogdan Raducanu edit   pdf
doi  openurl
  Title Rendering ground truth data sets to detect shadows cast by static objects in outdoors Type Journal Article
  Year 2014 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 70 Issue 1 Pages (up) 557-571  
  Keywords Synthetic ground truth data set; Sun position; Shadow detection; Static objects shadow detection  
  Abstract In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically.  
  Address  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1380-7501 ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number Admin @ si @ ISR2014 Serial 2229  
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Author David Masip; Jordi Vitria edit  openurl
  Title Shared Feature Extraction for Nearest Neighbor Face Recognition Type Journal
  Year 2008 Publication IEEE Transactions on Neural Networks Abbreviated Journal  
  Volume 19 Issue 4 Pages (up) 586–595  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ MaV2008 Serial 944  
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Author Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol edit  doi
openurl 
  Title Minimal Design of Error-Correcting Output Codes Type Journal Article
  Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 33 Issue 6 Pages (up) 693-702  
  Keywords Multi-class classification; Error-correcting output codes; Ensemble of classifiers  
  Abstract IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers.
 
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0167-8655 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB; OR;HuPBA;MV Approved no  
  Call Number Admin @ si @ BEB2011a Serial 1800  
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Author A. Martinez; Jordi Vitria edit  doi
openurl 
  Title Learning mixture models using a genetic version of the EM algorithm. Type Journal Article
  Year 2000 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 21 Issue 8 Pages (up) 759–769  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ MVi2000 Serial 335  
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