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| Author | Sergio Escalera | ||||
| Title | Fast traffic model matching and recognition on gray-scale images | Type | Report | ||
| Year | 2005 | Publication | CVC Technical Report #84 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Address | CVC (UAB) | ||||
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| 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 | Conference | |||
| Notes | MILAB; HuPBA | Approved | no | ||
Call Number  |
BCNPCL @ bcnpcl @ Esc2005 | Serial | 572 | ||
| Permanent link to this record | |||||
| Author | Sergio Escalera; Petia Radeva; Jordi Vitria; Xavier Baro; Bogdan Raducanu | ||||
| Title | Modelling and Analyzing Multimodal Dyadic Interactions Using Social Networks | Type | Conference Article | ||
| Year | 2010 | Publication | 12th International Conference on Multimodal Interfaces and 7th Workshop on Machine Learning for Multimodal Interaction. | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Social interaction; Multimodal fusion, Influence model; Social network analysis | ||||
| Abstract | Social network analysis became a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from
multimodal dyadic interactions. First, speech detection is performed through an audio/visual fusion scheme based on stacked sequential learning. In the audio domain, speech is detected through clusterization of audio features. Clusters are modelled by means of an One-state Hidden Markov Model containing a diagonal covariance Gaussian Mixture Model. In the visual domain, speech detection is performed through differential-based feature extraction from the segmented mouth region, and a dynamic programming matching procedure. Second, in order to model the dyadic interactions, we employed the Influence Model whose states encode the previous integrated audio/visual data. Third, the social network is extracted based on the estimated influences. For our study, we used a set of videos belonging to New York Times’ Blogging Heads opinion blog. The results are reported both in terms of accuracy of the audio/visual data fusion and centrality measures used to characterize the social network. |
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| Address | Beijing (China) | ||||
| 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 | ICMI-MLI | ||
| Notes | OR;MILAB;HUPBA;MV | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ ERV2010 | Serial | 1427 | ||
| Permanent link to this record | |||||
| Author | Sergio Escalera; Petia Radeva; Oriol Pujol | ||||
| Title | Complex Salient Regions for Computer Vision Problems | Type | Conference Article | ||
| Year | 2007 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshop on | Abbreviated Journal | |
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| Abstract | |||||
| Address | Minneapolis (USA) | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ ERP2007 | Serial | 908 | ||
| Permanent link to this record | |||||
| 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. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Area | Expedition | Conference | |||
| Notes | MILAB;HUPBA | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ EPR2010e | Serial | 1338 | ||
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| Author | Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria; Maria Teresa Anguera | ||||
| Title | Automatic Detection of Dominance and Expected Interest | Type | Journal Article | ||
| Year | 2010 | Publication | EURASIP Journal on Advances in Signal Processing | Abbreviated Journal | EURASIPJ |
| Volume | Issue | Pages | 12 | ||
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| Abstract | Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1110-8657 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | OR;MILAB;HUPBA;MV | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ EPR2010d | Serial | 1283 | ||
| Permanent link to this record | |||||
| 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 | |
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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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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1532-4435 | ISBN | Medium | ||
| Area | Expedition | 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 | 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 |
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| 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. | ||||
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| Publisher | Place of Publication | Editor | |||
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| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | 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 | 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 |
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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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| 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 | 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 | 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 | |
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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. | ||||
| Address | Reykjavik (Iceland) | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-02325-5 | Medium | |
| Area | Expedition | Conference | MCS | ||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ EPR2009d | Serial | 1190 | ||
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| Author | Sergio Escalera; Eloi Puertas; Petia Radeva; Oriol Pujol | ||||
| Title | Multimodal laughter recognition in video conversations | Type | Conference Article | ||
| Year | 2009 | Publication | 2nd IEEE Workshop on CVPR for Human communicative Behavior analysis | Abbreviated Journal | |
| Volume | Issue | Pages | 110–115 | ||
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| Abstract | Laughter detection is an important area of interest in the Affective Computing and Human-computer Interaction fields. In this paper, we propose a multi-modal methodology based on the fusion of audio and visual cues to deal with the laughter recognition problem in face-to-face conversations. The audio features are extracted from the spectogram and the video features are obtained estimating the mouth movement degree and using a smile and laughter classifier. Finally, the multi-modal cues are included in a sequential classifier. Results over videos from the public discussion blog of the New York Times show that both types of features perform better when considered together by the classifier. Moreover, the sequential methodology shows to significantly outperform the results obtained by an Adaboost classifier. | ||||
| Address | Miami (USA) | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 2160-7508 | ISBN | 978-1-4244-3994-2 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ EPR2009c | Serial | 1188 | ||
| Permanent link to this record | |||||
| Author | Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria | ||||
| Title | Measuring Interest of Human Dyadic Interactions | Type | Conference Article | ||
| Year | 2009 | Publication | 12th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | 202 | Issue | Pages | 45-54 | |
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| Abstract | In this paper, we argue that only using behavioural motion information, we are able to predict the interest of observers when looking at face-to-face interactions. We propose a set of movement-related features from body, face, and mouth activity in order to define a set of higher level interaction features, such as stress, activity, speaking engagement, and corporal engagement. Error-Correcting Output Codes framework with an Adaboost base classifier is used to learn to rank the perceived observer's interest in face-to-face interactions. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers. In particular, the learning system shows that stress features have a high predictive power for ranking interest of observers when looking at of face-to-face interactions. | ||||
| Address | Cardona (Spain) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-60750-061-2 | Medium | ||
| Area | Expedition | Conference | CCIA | ||
| Notes | OR;MILAB;HuPBA;MV | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ EPR2009b | Serial | 1182 | ||
| Permanent link to this record | |||||
| 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 |
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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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| Area | Expedition | Conference | |||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ EPR2009a | Serial | 1153 | ||
| Permanent link to this record | |||||
| 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 | |
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| Abstract | |||||
| Address | Santorini (Greece) | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICVS | ||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ EPR2008c | Serial | 963 | ||
| Permanent link to this record | |||||
| 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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| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ EPR2008b | Serial | 960 | ||
| Permanent link to this record | |||||
| 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 | |
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| Address | Madeira (Portugal) | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ EPR2008a | Serial | 964 | ||
| Permanent link to this record | |||||