Document Expansion for Text-Based Image Retrieval at CLEF 2009
نویسندگان
چکیده
In this paper, we describe and analyze our participation in the WikipediaMM task at CLEF 2009. Our main efforts concern the expansion of the image metadata from the Wikipedia abstracts collection DBpedia. In our experiments, we use the Okapi feedback algorithm for document expansion. Compared with our text retrieval baseline, our best document expansion RUN improves MAP by 17.89%. As one of our conclusions, document expansion from external resource can play an effective factor in the image metadata retrieval task.
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