Large scale visual-based event matching

Riadh Trad 1 Alexis Joly 1, 2 Nozha Boujemaa 1
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Organizing media according to real-life events is attracting interest in the multimedia community. Event-centric indexing approaches are very promising for discovering more complex relationships between data. In this paper we introduce a new visual-based method for retrieving events in photo collections, typically in the context of User Generated Contents. Given a query event record, represented by a set of photos, our method aims to retrieve other records of the same event, typically generated by distinct users. Similarly to what is done in state-of-the-art object retrieval systems, we propose a two-stage strategy combining an efficient visual indexing model with a spatiotemporal verification re-ranking stage to improve query performance. For efficiency and scalability concerns, we implemented the proposed method according to the MapReduce programming model using Multi-Probe Locality Sensitive Hashing. Experiments were conducted on LastFM-Flickr dataset for distinct scenarios, including event retrieval, automatic annotation and tags suggestion. As one result, our method is able to suggest the correct event tag over 5 suggestions with a 72% success rate.
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Communication dans un congrès
ICMR'11 - International Conference on Multimedia Retrieval, Apr 2011, Trento, Italy. ACM, pp.53:1--53:7, 2011, 〈http://www.icmr2011.org/〉. 〈10.1145/1991996.1992049〉
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Riadh Trad, Alexis Joly, Nozha Boujemaa. Large scale visual-based event matching. ICMR'11 - International Conference on Multimedia Retrieval, Apr 2011, Trento, Italy. ACM, pp.53:1--53:7, 2011, 〈http://www.icmr2011.org/〉. 〈10.1145/1991996.1992049〉. 〈hal-00642210〉

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