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Author |
Xavier Baro; Sergio Escalera; Petia Radeva; Jordi Vitria |
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Title |
Visual Content Layer for Scalable Recognition in Urban Image Databases, Internet Multimedia Search and Mining |
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Conference Article |
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2009 |
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10th IEEE International Conference on Multimedia and Expo |
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1616–1619 |
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Rich online map interaction represents a useful tool to get multimedia information related to physical places. With this type of systems, users can automatically compute the optimal route for a trip or to look for entertainment places or hotels near their actual position. Standard maps are defined as a fusion of layers, where each one contains specific data such height, streets, or a particular business location. In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (> 500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. This allows an efficient and scalable way of accessing maps by visual content. |
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New York (USA) |
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978-1-4244-4291-1 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ BER2009 |
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1189 |
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Author |
D. Jayagopi; Bogdan Raducanu; D. Gatica-Perez |
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Title |
Characterizing conversational group dynamics using nonverbal behaviour |
Type |
Conference Article |
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Year |
2009 |
Publication |
10th IEEE International Conference on Multimedia and Expo |
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370–373 |
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This paper addresses the novel problem of characterizing conversational group dynamics. It is well documented in social psychology that depending on the objectives a group, the dynamics are different. For example, a competitive meeting has a different objective from that of a collaborative meeting. We propose a method to characterize group dynamics based on the joint description of a group members' aggregated acoustical nonverbal behaviour to classify two meeting datasets (one being cooperative-type and the other being competitive-type). We use 4.5 hours of real behavioural multi-party data and show that our methodology can achieve a classification rate of upto 100%. |
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New York, USA |
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1945-7871 |
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978-1-4244-4290-4 |
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OR;MV |
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BCNPCL @ bcnpcl @ JRG2009 |
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1217 |
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