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Author Sergio Escalera; Oriol Pujol; Petia Radeva edit  url
doi  openurl
  Title Re-coding ECOCs without retraining Type Journal Article
  Year 2010 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 31 Issue 7 Pages 555–562  
  Keywords  
  Abstract A standard way to deal with multi-class categorization problems is by the combination of binary classifiers in a pairwise voting procedure. Recently, this classical approach has been formalized in the Error-Correcting Output Codes (ECOC) framework. In the ECOC framework, the one-versus-one coding demonstrates to achieve higher performance than the rest of coding designs. The binary problems that we train in the one-versus-one strategy are significantly smaller than in the rest of designs, and usually easier to be learnt, taking into account the smaller overlapping between classes. However, a high percentage of the positions coded by zero of the coding matrix, which implies a high sparseness degree, does not codify meta-class membership information. In this paper, we show that using the training data we can redefine without re-training, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information helps the system to increase its generalization capability. Moreover, the new re-coding strategy is generalized to be applied over any binary code. The results over several UCI Machine Learning repository data sets and two real multi-class problems show that performance improvements can be obtained re-coding the classical one-versus-one and Sparse random designs compared to different state-of-the-art ECOC configurations.  
  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 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HUPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPR2010e Serial 1338  
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Author Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol edit  doi
openurl 
  Title Online Error-Correcting Output Codes Type Journal Article
  Year 2011 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 32 Issue 3 Pages 458-467  
  Keywords  
  Abstract IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier.
 
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication North Holland 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 @ EMP2011 Serial 1714  
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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 (down) PRL  
  Volume 33 Issue 6 Pages 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 Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Sergio Escalera; Xavier Baro; Oriol Pujol; Cecilio Angulo edit   pdf
doi  openurl
  Title Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D Type Journal Article
  Year 2014 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 50 Issue 1 Pages 112-121  
  Keywords RGB-D; Bag-of-Words; Dynamic Time Warping; Human Gesture Recognition  
  Abstract PATREC5825
We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MV; 605.203 Approved no  
  Call Number Admin @ si @ HBP2014 Serial 2353  
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Author Albert Clapes; Miguel Reyes; Sergio Escalera edit   pdf
url  doi
openurl 
  Title Multi-modal User Identification and Object Recognition Surveillance System Type Journal Article
  Year 2013 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 34 Issue 7 Pages 799-808  
  Keywords Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning  
  Abstract We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.  
  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 ISBN Medium  
  Area Expedition Conference  
  Notes HUPBA; 600.046; 605.203;MILAB Approved no  
  Call Number Admin @ si @ CRE2013 Serial 2248  
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