Abstract
This paper describes the work of the LIG for ImageCLEF 2008. For ImageCLEFPhoto, two non diversified runs (text only and text + image), and two diversified runs were officially submitted. We add in this paper results on image only runs. The text retrieval part is based on a language model of Information Retrieval, and the image part uses RGB histograms. Text+image results are obtained by late fusion, by merging text and image results. We tested three strategies for promoting diversity using date/location or visual features. Diversification on image only runs does not perform well. Diversification on image and text+image outperforms non diversified runs. In a second part, this paper describes the runs and results obtained by the LIG at ImageCLEFmed 2008. This contribution incorporates knowledge in the language modeling approach to information retrieval (IR) through the graph modeling approach proposed in . Our model makes use of the textual part of the corpus and of the medical knowledge found in the Unified Medical Language System (UMLS) knowledge sources. And the model is extended to combine different graph detection methods on queries and documents. The results show that detection combination improves the performances.
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Maisonnasse, L., Mulhem, P., Gaussier, E., Chevallet, J.P. (2009). LIG at ImageCLEF 2008. In: Peters, C., et al. Evaluating Systems for Multilingual and Multimodal Information Access. CLEF 2008. Lecture Notes in Computer Science, vol 5706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04447-2_89
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DOI: https://doi.org/10.1007/978-3-642-04447-2_89
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