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IRIM at TRECVID 2012: Semantic Indexing and Instance Search

Nicolas Ballas 1 Benjamin Labbé 1 Aymen Shabou 1 Hervé Le Borgne 1 Philippe-Henri Gosselin 2 Miriam Redi 3 Bernard Merialdo 3 Hervé Jégou 4 Jonathan Delhumeau 4 Rémi Vieux 5 Boris Mansencal 5 Jenny Benois-Pineau 5 Stéphane Ayache 6 Abdelkader Hamadi 7 Bahjat Safadi 7 Franck Thollard 8 Nadia Derbas 7 Georges Quénot 8, * Hervé Bredin 9 Matthieu Cord 10 Boyang Gao 11 Chao Zhu 12 Yuxing Tang 12 Emmanuel Dellandrea 11 Charles-Edmond Bichot 11 Liming Chen 11 Alexandre Benoit 13 Patrick Lambert 13 Tiberius Strat 13 Joseph Razik 14 Sébastien Paris 14 Hervé Glotin 14, 15 Tran Ngoc Trung 16, 17 Dijana Petrovska-Delacrétaz 16, 17 Gérard Chollet 18 Andrei Stoian 19 Michel Crucianu 19
* Corresponding author
2 MIDI - Multimedia Indexation and Data Integration
ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
4 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
10 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
11 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : The IRIM group is a consortium of French teams work- ing on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2012 se- mantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likeli- hood of a video shot to contain a target concept. These scores are then used for producing a ranked list of im- ages or shots that are the most likely to contain the tar- get concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classi cation, fusion of descriptor variants, higher-level fusion, and re-ranking. We evaluated a number of dif- ferent descriptors and tried di erent fusion strategies. The best IRIM run has a Mean Inferred Average Pre- cision of 0.2378, which ranked us 4th out of 16 partici- pants. For the instance search task, our approach uses two steps. First individual methods of participants are used to compute similrity between an example image of in- stance and keyframes of a video clip. Then a two-step fusion method is used to combine these individual re- sults and obtain a score for the likelihood of an instance to appear in a video clip. These scores are used to ob- tain a ranked list of clips the most likely to contain the queried instance. The best IRIM run has a MAP of 0.1192, which ranked us 29th on 79 fully automatic runs.
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Nicolas Ballas, Benjamin Labbé, Aymen Shabou, Hervé Le Borgne, Philippe-Henri Gosselin, et al.. IRIM at TRECVID 2012: Semantic Indexing and Instance Search. TRECVID - TREC Video Retrieval Evaluation workshop, Nov 2012, Gaithersburg, MD, United States. 12p. ⟨hal-00770258⟩

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