@Article{SergioEscalera2012,
author="Sergio Escalera
and Xavier Baro
and Jordi Vitria
and Petia Radeva
and Bogdan Raducanu",
title="Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction",
journal="Sensors",
year="2012",
publisher="Molecular Diversity Preservation International",
volume="12",
number="2",
pages="1702--1719",
abstract="IF=1.77 (2010)Social interactions are a very important component in people{\'i}s lives. Social network analysis has become 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. For our study, we used a set of videos belonging to New York Times{\'i} Blogging Heads opinion blog.The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The links{\'i} weights are a measure of the {\`i}influence{\^i} a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.",
optnote="MILAB; OR;HuPBA;MV",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1885), last updated on Wed, 15 May 2013 08:39:03 +0200",
doi="10.3390/s120201702",
file=":http://refbase.cvc.uab.es/files/EBV2012.pdf:PDF"
}