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Conference Papers Year : 2012

IRIM - Indexation et Recherche d'Information Multimedia GDR-ISIS

Nicolas Ballas
  • Function : Author
  • PersonId : 767569
  • IdRef : 176879218
Benjamin Labbé
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Aymen Shabou
  • Function : Author
Hervé Le Borgne
Philippe Gosselin
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Miriam Redi
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  • PersonId : 765821
  • IdRef : 171913272
Bernard Merialdo
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Hervé Jégou
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Jonathan Delhumeau
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Rémi Vieux
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Boris Mansencal
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Jenny Benois-Pineau
Stéphane Ayache
Abdelkader Haadi
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Bahjat Safadi
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Franck Thollard
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Nadia Derbas
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Georges Quénot
Hervé Bredin
Matthieu Cord
  • Function : Author
Boyang Gao
  • Function : Author
Chao Zhu
  • Function : Author
Yuxing Tang
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Charles-Edmond Bichot
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  • PersonId : 744760
  • IdHAL : cebichot
Liming Chen
Alexandre Benoit
Patrick Lambert
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Tiberius Strat
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Joseph Razik
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  • PersonId : 764010
  • IdRef : 120118610
Sébastion Paris
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Hervé Glotin
Dijana Petrovska-Delacrétaz
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Andrei Stoian
  • Function : Author
Michel Crucianu
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Abstract

The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2012 semantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likelihood of a video shot to contain a target concept. These scores are then used for producing a ranked list of images or shots that are the most likely to contain the target concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classification, fusion of descriptor variants, higher-level fusion, and re-ranking. We evaluated a number of different descriptors and tried dierent fusion strategies. The best IRIM run has a Mean Inferred Average Precision of 0.2378, which ranked us 4th out of 16 participants. 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 instance and keyframes of a video clip. Then a two-step fusion method is used to combine these individual results and obtain a score for the likelihood of an instance to appear in a video clip. These scores are used to obtain 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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Dates and versions

hal-02411919 , version 1 (15-12-2019)

Identifiers

  • HAL Id : hal-02411919 , version 1

Cite

Nicolas Ballas, Benjamin Labbé, Aymen Shabou, Hervé Le Borgne, Philippe Gosselin, et al.. IRIM - Indexation et Recherche d'Information Multimedia GDR-ISIS. TRECVID, Nov 2012, Gaitherburg, États-Unis. ⟨hal-02411919⟩
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