A selective weighted late fusion for visual concept recognition

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 second out of the 79 submitted runs, and a MiAP of 43.67% that ranked first out of the 80 submitted runs in 2012.