Late fusion of heterogeneous methods for multimedia image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Annotation-based expansion and late fusion of mixed methods for multimedia image retrieval
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Diversity promotion: is reordering top-ranked documents sufficient?
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
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Submissions to the photographic retrieval task of the ImageCLEF 2007 evaluation and improvements of our methods that were tested and evaluated after the official benchmark. We use our image retrieval system FIRE to combine a set of different image descriptors. The most important step in combining descriptors is to find a suitable weighting. Here, we evaluate empirically tuned linear combinations, a trained logistic regression model, and support vector machines to fuse the different descriptors. Additionally, clustered SIFT histograms are evaluated for the given task and show very good results --- both, alone and in combination with other features. A clear improvement over our evaluation performance is shown consistently over different combination schemes and feature sets.