LIG at ImageCLEFphoto 2008

This working notes describe the runs and results obtained by the LIG at ImageCLEFphoto 2008. The submitted runs are: two runs (text only and text+image) without diversification on classes, and two runs (text only and text+image) with class diversification were submitted. The text retrieval is based on language model of Information Retrieval, and the image part is processed using RGB histograms on 9 image blocks with a similarity value based on Jeffrey divergence. Results using text+image are obtained by a linear combination of normalized results on text and image. The diversification is based on clusters, according to the cluster given in the queries. When the cluster name is not directly extracted from the images (like city or country), we apply a visual clustering. Not surprisingly, the cluster recall at 20 (i.e., cr(20)) results are higher for the runs that include diversification. On the other hand, the precision at 20 and the mean average precision results are higher without diversification on our runs, for both text only and image+text results.