LIG-MRIM at Image Photo Annotation Task in ImageCLEF 2011
Abstract
We describe in this paper the different approaches tested for the Photo Annotation task for CLEF 2011. We experimented state of the art techniques, by proposing late fusions of several classifiers trained on several features extracted from the images. The classifiers are SVMs and the late fusion is a simple addition of classification probabilities coming from the SVMs. The results obtained place our runs in the middle of the pack, with our best visual-based MAP at 0.337 We also integrated of Flickr human annotations, leading to a large increase of the MAP with a value of 0.377.
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