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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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2010 |
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IEEE on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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32 |
Issue  |
1 |
Pages |
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 |
Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Sergio Escalera; Xavier Baro; Oriol Pujol; Cecilio Angulo |
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Title |
Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D |
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Journal Article |
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2014 |
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Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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50 |
Issue |
1 |
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112-121 |
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RGB-D; Bag-of-Words; Dynamic Time Warping; Human Gesture Recognition |
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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. |
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HuPBA;MV; 605.203 |
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Admin @ si @ HBP2014 |
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2353 |
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Author |
Victor Ponce; Sergio Escalera; Marc Perez; Oriol Janes; Xavier Baro |
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Title |
Non-Verbal Communication Analysis in Victim-Offender Mediations |
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Journal Article |
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2015 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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67 |
Issue |
1 |
Pages |
19-27 |
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Keywords |
Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning |
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We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals. |
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HuPBA;MV |
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no |
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Admin @ si @ PEP2015 |
Serial |
2583 |
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Author |
Victor Ponce; Mario Gorga; Xavier Baro; Petia Radeva; Sergio Escalera |
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Title |
Análisis de la expresión oral y gestual en proyectos fin de carrera vía un sistema de visión artificial |
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2011 |
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ReVisión |
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4 |
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1 |
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La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación. |
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1989-1199 |
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HuPBA; MILAB;MV |
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no |
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Admin @ si @ PGB2011d |
Serial |
2514 |
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Author |
Alvaro Cepero; Albert Clapes; Sergio Escalera |
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Title |
Automatic non-verbal communication skills analysis: a quantitative evaluation |
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Journal Article |
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2015 |
Publication |
AI Communications |
Abbreviated Journal |
AIC |
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28 |
Issue |
1 |
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87-101 |
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Social signal processing; human behavior analysis; multi-modal data description; multi-modal data fusion; non-verbal communication analysis; e-Learning |
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The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions. |
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0921-7126 |
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HUPBA;MILAB |
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no |
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Admin @ si @ CCE2015 |
Serial |
2549 |
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