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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a novel framework to detect and classify objects in cluttered scenes Type Miscellaneous
Year 2006 Publication 18th International Conference on Pattern Recognition (ICPR´06), 4: 104–107, ISBN: 0–7695–2521–0 Abbreviated Journal
Volume Issue Pages
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Abstract
Address Hong Kong
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 (up) Conference
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2006a Serial 692
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title ECOC-ONE: A novel coding and decoding strategy Type Miscellaneous
Year 2006 Publication 18th International Conference on Pattern Recognition (ICPR´06), 3: 578–581, ISBN: 0–7695–2521–0 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Hong Kong
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 (up) Conference
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2006b Serial 693
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Decoding of Ternary Error Correcting Output Codes Type Book Chapter
Year 2006 Publication 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 753–763 Abbreviated Journal
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Abstract
Address Cancun (Mexico)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition (up) Conference
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2006e Serial 696
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Robust Complex Salient Regions Type Book Chapter
Year 2007 Publication 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4478:113–121 Abbreviated Journal
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Abstract
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 (up) Conference
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2007b Serial 906
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a Novel Framework to Detect and Classify Objects in Cluttered Scenes Type Journal
Year 2007 Publication Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
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 (up) Conference
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2007c Serial 907
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Traffic Sign Classification using Error Correcting Techniques Type Conference Article
Year 2007 Publication 2nd International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume Issue Pages 281–285
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Abstract
Address Barcelona (Spain)
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 (up) Conference VISAPP
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2007a Serial 909
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Detection of Complex Salient Regions Type Journal
Year 2008 Publication EURASIP Journal on Advances in Signal Processing, vol. 2008, article ID451389, 11 pages Abbreviated Journal
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Area Expedition (up) Conference
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2008b Serial 960
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Sub-Class Error-Correcting Output Codes Type Book Chapter
Year 2008 Publication Computer Vision Systems. 6th International Conference Abbreviated Journal
Volume 5008 Issue Pages 494–504
Keywords
Abstract
Address Santorini (Greece)
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 (up) Conference ICVS
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2008c Serial 963
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Loss-Weighted Decoding for Error-Correcting Output Coding Type Conference Article
Year 2008 Publication 3rd International Conference on Computer Vision Theory and Applications, Abbreviated Journal
Volume 2 Issue Pages 117–122
Keywords
Abstract
Address Madeira (Portugal)
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 (up) Conference VISAPP
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2008a Serial 964
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes Type Journal Article
Year 2009 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 30 Issue 3 Pages 285–297
Keywords
Abstract Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied.
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 (up) Conference
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2009a Serial 1153
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Recoding Error-Correcting Output Codes Type Conference Article
Year 2009 Publication 8th International Workshop of Multiple Classifier Systems Abbreviated Journal
Volume 5519 Issue Pages 11–21
Keywords
Abstract One of the most widely applied techniques to deal with multi- class categorization problems is the pairwise voting procedure. Recently, this classical approach has been embedded in the Error-Correcting Output Codes framework (ECOC). This framework is based on a coding step, where a set of binary problems are learnt and coded in a matrix, and a decoding step, where a new sample is tested and classified according to a comparison with the positions of the coded matrix. In this paper, we present a novel approach to redefine without retraining, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information increases the generalization capability of the system. Moreover, the final classification can be tuned with the inclusion of a weighting matrix in the decoding step. The approach has been validated over several UCI Machine Learning repository data sets and two real multi-class problems: traffic sign and face categorization. The results show that performance improvements are obtained when comparing the new approach to one of the best ECOC designs (one-versus-one). Furthermore, the novel methodology obtains at least the same performance than the one-versus-one ECOC design.
Address Reykjavik (Iceland)
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-02325-5 Medium
Area Expedition (up) Conference MCS
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2009d Serial 1190
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Traffic sign recognition system with β -correction Type Journal Article
Year 2010 Publication Machine Vision and Applications Abbreviated Journal MVA
Volume 21 Issue 2 Pages 99–111
Keywords
Abstract Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.
Address
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0932-8092 ISBN Medium
Area Expedition (up) Conference
Notes MILAB;HUPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2010a Serial 1276
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title On the Decoding Process in Ternary Error-Correcting Output Codes Type Journal Article
Year 2010 Publication IEEE on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 32 Issue 1 Pages 120–134
Keywords
Abstract A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-correcting output codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a ldquodo not carerdquo symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI machine learning repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved.
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 0162-8828 ISBN Medium
Area Expedition (up) Conference
Notes MILAB;HUPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2010b Serial 1277
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Error-Correcting Output Codes Library Type Journal Article
Year 2010 Publication Journal of Machine Learning Research Abbreviated Journal JMLR
Volume 11 Issue Pages 661-664
Keywords
Abstract (Feb):661&#8722;664
In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier.
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 1532-4435 ISBN Medium
Area Expedition (up) Conference
Notes MILAB;HUPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2010c Serial 1286
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Re-coding ECOCs without retraining Type Journal Article
Year 2010 Publication Pattern Recognition Letters Abbreviated Journal 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 (up) Conference
Notes MILAB;HUPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2010e Serial 1338
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