Document expansion for text-based image retrieval at WikipediaMM 2010

We describe and analyze our participation in the Wikipedi- aMM task at ImageCLEF 2010. Our approach is based on text-based image retrieval using information retrieval techniques on the metadata documents of the images. We submitted two English monolingual runs and one multilingual run. The monolingual runs used the query to retrieve the metadata document with the query and document in the same language; the multilingual run used queries in one language to search the metadata provided in three languages. The main focus of our work was using the English query to retrieve images based on the English meta-data. For these experiments the English metadata data was expanded using an external resource - DBpedia. This study expanded on our application of document expansion in our previous participation in Image-CLEF 2009. In 2010 we combined document expansion with a document reduction technique which aimed to include only topically important words to the metadata. Our experiments used the Okapi feedback algorithm for document expansion and Okapi BM25 model for retrieval. Experimental results show that combining document expansion with the document reduction method give the best overall retrieval results