Abstract
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.
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Gass, T., Weyand, T., Deselaers, T., Ney, H. (2008). FIRE in ImageCLEF 2007: Support Vector Machines and Logistic Models to Fuse Image Descriptors for Photo Retrieval. In: Peters, C., et al. Advances in Multilingual and Multimodal Information Retrieval. CLEF 2007. Lecture Notes in Computer Science, vol 5152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85760-0_60
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DOI: https://doi.org/10.1007/978-3-540-85760-0_60
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