Cross-media feedback strategies: merging text and image information to improve image retrieval

Authors:
Romaric Besançon;Patrick Hède;Pierre-Alain Moellic;Christian Fluhr
Affiliations:
LIC2M (Multilingual Multimedia Knowledge Engineering Laboratory), CEA-LIST, Fontenay-aux-Roses Cedex, France;LIC2M (Multilingual Multimedia Knowledge Engineering Laboratory), CEA-LIST, Fontenay-aux-Roses Cedex, France;LIC2M (Multilingual Multimedia Knowledge Engineering Laboratory), CEA-LIST, Fontenay-aux-Roses Cedex, France;LIC2M (Multilingual Multimedia Knowledge Engineering Laboratory), CEA-LIST, Fontenay-aux-Roses Cedex, France
Venue:
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Year:
2004

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Abstract

The CEA-LIST/LIC2M develops both cross-language information retrieval systems and content-based image retrieval systems. The ad hoc and medical tasks of the ImageCLEF campaign offered us the opportunity to perform some experiments on merging the results of the two systems. The results obtained show that the performance of each system highly depends on the corpus and the task: feedback strategies can improve the results, but the parameters used are to be tuned according to the confidence of each system on the task and corpus: for the ad hoc task, text retrieval performs good whereas results of image retrieval are poor. On the other hand, for the medical task, the image retrieval performs better, and text retrieval can improve overall results only with reinforcement strategies.