Abstract
For this year’s Image CLEF Photo Retrieval task, we investigated the effectiveness of 1) image content-based retrieval, 2) text-based retrieval, and 3) integrated text and image retrieval. We investigated whether the clustering of results can increase diversity by returning as many different clusters of images in the results as possible. Our image system used the FIRE engine to extract image features such as color, texture, and shape from a data collection consisting of about half a million images. The text-retrieval backend used Lucene to extract texts from image annotations, title, and cluster tags. Our results revealed 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 increased precision, but only to a certain extent. Clustering also improved diversity, only in our text-based clustering runs.
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Zhu, Q., Inkpen, D. (2010). Clustering for Text and Image-Based Photo Retrieval at CLEF 2009. In: Peters, C., et al. Multilingual Information Access Evaluation II. Multimedia Experiments. CLEF 2009. Lecture Notes in Computer Science, vol 6242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15751-6_17
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DOI: https://doi.org/10.1007/978-3-642-15751-6_17
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