This work was supported in part by Spanish projects BUSCAMEDIA under CEN-20091026, MA2VICMR under S2009/TIC-1542, and MCYT under TEC2009-12980. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Francesco G. B. De Natale. X. Benavent, J. Benavent, and E. de Ves are with the Computer Sci-

The main goal of this paper it is to present our experiments in Im-ageCLEF 2009 Campaign (photo retrieval task). In 2008 we proved empirically that the Text-based Image Retrieval (TBIR) methods defeats the Content-based Image Retrieval CBIR "quality" of results, so this time we developed several experiments in which the CBIR helps the TBIR. The TBIR System [6] main improvement is the named-entity sub-module. In case of the CBIR system [3] the number of low-level features has been increased from the 68 component used at ImageCLEF 2008 up to 114 components, and only the Mahalanobis distance has been used. We propose an ad-hoc management of the topics delivered, and the generation of XML structures for 0.5 million captions of the photographs (corpus) delivered. Two different merging algorithms were developed and the third one tries to improve our previous cluster level results promoting the diversity. Our best run for precision metrics appeared in position 16 th , in the 19 th for MAP score, and for diversity value in position 11 th , for a total of 84 submitted experiments. Our best and "only textual" experiment was the 6 th one over 41.

Abstract. This paper describes the participation of the MIRACLE team 1 at the ImageCLEF Photographic Retrieval task of CLEF 2008. We succeeded in submitting 41 runs. Obtained results from text-based retrieval are better than content-based as previous experiments in the MIRACLE team campaigns [5,6] using different software. Our main aim was to experiment with several merging approaches to fuse text-based retrieval and content-based retrieval results, and it happened that we improve the text-based baseline when applying one of the three merging algorithms, although visual results are lower than textual ones.

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