SINAI at ImageCLEF 2006.
Manuel Carlos Díaz–Galiano, Miguel Ángel García Cumbreras, María Teresa Martín Valdivia, Arturo Montejo‐Ráez, Luís Alfonso Ureña López · CLEF (Working Notes) · 2006
This paper describes SINAI team participation in the ImageCLEF campaign. The SINAI research group participated in both the ad hoc task and the medical task. The experiments accomplished in both tasks result from very difierent approaches. For the adhoc task the main IR system used is the same as that of the 2005 ImageCLEF adhoc task. The improvement of the adhoc system is a new Machine Translation system that works with several translators and implements several heuristics. We have participated in the English monolingual task and in six bilingual tasks for the languages: Dutch, French, German, Italian, Portuguese and Spanish. The results obtained shown that the English monolingual results are good (0,2234 is our best result) and there is a loss of precision with the bilingual runs and some languages like German or Spanish works better than others, because of the translations. For the medical task, this year we carried out new and very difierent experiments to imageCLEFmed2005 ones. First of all, we have processed the set of collections using Information Gain (IG) to determine which are the best tags that should be considered in the indexing process. These tags are those supposed to provide the most relevant and non-redundant information, and have been selected automatically according to our information-based strategy along with the data and relevance assessments from last year. This year, our goal was to analyze how tag selection may contribute to the quality of flnal results. In order to select reduced set of tags we have computed IG. 11 difierent collections were generated according to the percentage of tags with highest IG value. Finally, only results related to experiments with selections over the 20%, 30% and 40% of available tags were submitted, since they reported best performance on 2005 data. Experiments using only textual query and using textual mixing with visual query have been submitted. For visual query we have used the GIFT lists provide by the organization. Surprisingly, the system performs better on the text retrieval alone than mixed textual and visual retrieval. On the other hand, we try show that information flltering through tag selection using information gain improves retrieval results without the need of a manual selection, but the obtained results are no conclusive. Unfortunately, the results obtained are not as successful as desired. Due to a computing processing mistake all our mixed runs obtain the same results than the visual GIFT baseline (0.0467). At the moment of writing of this paper we are modifying our system in order to solve this problem.