MIRACLE at ImageCLEFmed 2009: Reevaluating Strategies for Automatic Topic Expansion

For them, our central method is integrated combination multimodal retrieval applied to re- trieved documents sets of each visual and text features, after than our infor-

For the topics, we selected the English title as the query; for the metadata collection, the text including the image name, description, comment and caption was selected as the query

For this year’s Image CLEF Photo Retrieval task, we have prepared 5 submission runs to help us assess the effectiveness of 1) image content-based retrieval, and 2) text-

The SMART system was used to perform retrieval using a generalized vector space model that included the original text, the automatically assigned UMLS concepts, and the UMLS

To generate the feature vectors at different levels of abstraction, we extract both visual concept- based feature based on a “bag of concepts” model comprising color and texture

The number of visual runs in 2008 was much lower than in previous years, and the evolution is not as fast as with textual retrieval techniques.. Five groups submitted a total of

Specifically, our system is composed of five different modules: the textual (text-based) retrieval module, which indexes image annotations in order to search and find the most

The architecture of our system is composed of four different modules: the textual (text-based) retrieval module, which indexes medical case descriptions in order to search