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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Decoding of Ternary Error Correcting Output Codes |
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Book Chapter |
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2006 |
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11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 753–763 |
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Cancun (Mexico) |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPR2006e |
Serial |
696 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Robust Complex Salient Regions |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4478:113–121 |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2007b |
Serial |
906 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Sub-Class Error-Correcting Output Codes |
Type |
Book Chapter |
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Year |
2008 |
Publication |
Computer Vision Systems. 6th International Conference |
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Volume |
5008 |
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Pages |
494–504 |
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Santorini (Greece) |
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ICVS |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2008c |
Serial |
963 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Traffic Sign Classification using Error Correcting Techniques |
Type |
Conference Article |
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Year |
2007 |
Publication |
2nd International Conference on Computer Vision Theory and Applications |
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281–285 |
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Barcelona (Spain) |
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Conference |
VISAPP |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2007a |
Serial |
909 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Loss-Weighted Decoding for Error-Correcting Output Coding |
Type |
Conference Article |
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Year |
2008 |
Publication |
3rd International Conference on Computer Vision Theory and Applications, |
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Volume |
2 |
Issue |
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Pages |
117–122 |
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Madeira (Portugal) |
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VISAPP |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2008a |
Serial |
964 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Recoding Error-Correcting Output Codes |
Type |
Conference Article |
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Year |
2009 |
Publication |
8th International Workshop of Multiple Classifier Systems |
Abbreviated Journal |
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Volume |
5519 |
Issue |
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Pages |
11–21 |
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Keywords |
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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. |
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Reykjavik (Iceland) |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-02325-5 |
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MCS |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2009d |
Serial |
1190 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a Novel Framework to Detect and Classify Objects in Cluttered Scenes |
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Journal |
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2007 |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2007c |
Serial |
907 |
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| Permanent link to this record |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Detection of Complex Salient Regions |
Type |
Journal |
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Year |
2008 |
Publication |
EURASIP Journal on Advances in Signal Processing, vol. 2008, article ID451389, 11 pages |
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MILAB;HuPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2008b |
Serial |
960 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes |
Type |
Journal Article |
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Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
30 |
Issue |
3 |
Pages |
285–297 |
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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. |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2009a |
Serial |
1153 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Traffic sign recognition system with β -correction |
Type |
Journal Article |
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Year |
2010 |
Publication |
Machine Vision and Applications |
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MVA |
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21 |
Issue |
2 |
Pages |
99–111 |
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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. |
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Springer-Verlag |
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0932-8092 |
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MILAB;HUPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2010a |
Serial |
1276 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
On the Decoding Process in Ternary Error-Correcting Output Codes |
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Journal Article |
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Year |
2010 |
Publication |
IEEE on Pattern Analysis and Machine Intelligence |
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TPAMI |
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32 |
Issue |
1 |
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120–134 |
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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. |
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0162-8828 |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ EPR2010b |
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1277 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Error-Correcting Output Codes Library |
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Journal Article |
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Year |
2010 |
Publication |
Journal of Machine Learning Research |
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JMLR |
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11 |
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661-664 |
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Abstract |
(Feb):661−664
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. |
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1532-4435 |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ EPR2010c |
Serial |
1286 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Re-coding ECOCs without retraining |
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Journal Article |
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Year |
2010 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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31 |
Issue |
7 |
Pages |
555–562 |
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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. |
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Elsevier |
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MILAB;HUPBA |
Approved |
no |
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BCNPCL @ bcnpcl @ EPR2010e |
Serial |
1338 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a novel framework to detect and classify objects in cluttered scenes |
Type |
Miscellaneous |
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Year |
2006 |
Publication |
18th International Conference on Pattern Recognition (ICPR´06), 4: 104–107, ISBN: 0–7695–2521–0 |
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Hong Kong |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPR2006a |
Serial |
692 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
ECOC-ONE: A novel coding and decoding strategy |
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Miscellaneous |
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2006 |
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18th International Conference on Pattern Recognition (ICPR´06), 3: 578–581, ISBN: 0–7695–2521–0 |
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Keywords |
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Abstract |
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Address |
Hong Kong |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Editor |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Area |
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Expedition |
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Conference |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2006b |
Serial |
693 |
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