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Large scale visual-based event matching

Riadh Mohamed 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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Submitted on : Thursday, November 17, 2011 - 3:39:14 PM
Last modification on : Tuesday, September 6, 2022 - 4:56:06 PM
Long-term archiving on: : Monday, December 5, 2016 - 10:39:28 AM


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Riadh Mohamed Trad, Alexis Joly, Nozha Boujemaa. Large scale visual-based event matching. ICMR'11 - International Conference on Multimedia Retrieval, Apr 2011, Trento, Italy. pp.53:1--53:7, ⟨10.1145/1991996.1992049⟩. ⟨hal-00642210⟩



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