Clustering for Text and Image-based Photo Retrieval at CLEF 2009

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) textbased retrieval. We investigate whether the clustering of results can increase diversity by returning as many different clusters of images in the results as possible. Our image system uses the FIRE engine to extract image features such as color, texture, and shape from a database consisting of more than half a million images. The text-retrieval backend uses Lucene to extract texts from image annotations, title, and cluster tags. Our results reveal that among the three image features, color yields the highest retrieval precision, followed by shape, then texture. A combination of color extraction with text retrieval has the potential to increase precision, but only to a certain extent. Clustering also improves diversity in one of our clustering runs.