IRIM at TRECVID 2011: Semantic Indexing and Instance SearchReportar como inadecuado

IRIM at TRECVID 2011: Semantic Indexing and Instance Search - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

* Corresponding author 1 LIST - Laboratoire d-Intégration des Systèmes et des Technologies 2 ETIS - Equipes Traitement de l-Information et Systèmes 3 LIP - Laboratoire d-Imagerie Paramétrique 4 MIDI ETIS - Equipes Traitement de l-Information et Systèmes 5 Eurecom Sophia Antipolis 6 GIPSA-Services - GIPSA-Services GIPSA-lab - Grenoble Images Parole Signal Automatique 7 GIPSA-AGPIG - AGPIG GIPSA-DIS - Département Images et Signal 8 TEXMEX - Multimedia content-based indexing IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique 9 LaBRI - Laboratoire Bordelais de Recherche en Informatique 10 LIF - Laboratoire d-informatique Fondamentale de Marseille - UMR 6166 11 LIG - Laboratoire d-Informatique de Grenoble 12 MRIM - Modélisation et Recherche d’Information Multimédia Grenoble LIG - Laboratoire d-Informatique de Grenoble, Inria - Institut National de Recherche en Informatique et en Automatique 13 LTCI - Laboratoire Traitement et Communication de l-Information 14 MALIRE - Machine Learning and Information Retrieval LIP6 - Laboratoire d-Informatique de Paris 6 15 LISTIC - Laboratoire d-Informatique, Systèmes, Traitement de l-Information et de la Connaissance 16 LSIS - Laboratoire des Sciences de l-Information et des Systèmes 17 IUF - Institut Universitaire de France

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 2011 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, classification, fusion of descriptor variants, higher-level fusion, and re-ranking. We evaluated a number of dif- ferent descriptors and tried different fusion strategies. The best IRIM run has a Mean Inferred Average Pre- cision of 0.1387, which ranked us 5th out of 19 partic- ipants. For the instance search task, we we used both object based query and frame based query. We formu- lated the query in standard way as comparison of visual signatures either of object with parts of DB frames or as a comparison of visual signatures of query and DB frames. To produce visual signatures we also used two apporaches: the first one is the baseline Bag-Of-Visual- Words BOVW model based on SURF interest point descriptor; the second approach is a Bag-Of-Regions BOR model that extends the traditional notion of BOVW vocabulary not only to keypoint-based descrip- tors but to region based descriptors.

Keywords : descriptors Semantic Indexing Instance Search fusion classification high level features extraction

Autor: Bertrand Delezoide - Frédéric Precioso - Philippe-Henri Gosselin - Miriam Redi - Bernard Merialdo - Lionel Granjon - Denis Pell



Documentos relacionados