A Selective Weighted Late Fusion for Visual Concept Recognition

Ningning Liu, Emmanuel Dellandréa, Chao Zhu, Charles-Edmond Bichot, Liming Chen

Abstract: We propose in this paper 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 experts for each visual concept to be recognized. Experiments were conducted on the MIR Flickr image collection within the ImageCLEF 2011 Photo Annotation challenge. The results have brought to the fore the effectiveness of SWLF as it achieved a MiAP of 43.69 % for the detection of the 99 visual concepts which ranked 2 nd out of the 79 submitted runs, while our new variant of SWLF allows to reach a MiAP of 43.93 %.