A Selective Weighted Late Fusion for Visual Concept Recognition

Ningning Liu 1 Emmanuel Dellandréa 1 Bruno Tellez 1 Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : We propose a novel multimodal approach to automatically predict the visual concepts of images through an effective fusion of visual and textual features. It relies on a Selective Weighted Late Fusion (SWLF) scheme which, in optimizing an overall Mean interpolated Average Precision (MiAP), learns to automatically select and weight the best features for each visual concept to be recognized. Experiments were conducted on the MIR Flickr image collection within the ImageCLEF Photo Annotation challenge. The results have brought to the fore the effectiveness of SWLF as it achieved a MiAP of 43.69% in 2011 which ranked 2nd out of the 79 submitted runs, and a MiAP of 43.67% that ranked 1st out of the 80 submitted runs in 2012.
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Chapitre d'ouvrage
B. Ionescu et al. Fusion in Computer Vision, Springer, pp.1-28, 2014, <10.1007/978-3-319-05696-8_1>
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https://hal.archives-ouvertes.fr/hal-01301049
Contributeur : Équipe Gestionnaire Des Publications Si Liris <>
Soumis le : lundi 11 avril 2016 - 16:28:44
Dernière modification le : mardi 12 avril 2016 - 01:07:10

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Ningning Liu, Emmanuel Dellandréa, Bruno Tellez, Liming Chen. A Selective Weighted Late Fusion for Visual Concept Recognition. B. Ionescu et al. Fusion in Computer Vision, Springer, pp.1-28, 2014, <10.1007/978-3-319-05696-8_1>. <hal-01301049>

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