Abstract
We describe our approach to the ImageCLEFphoto 2007 task. The novelty of our method consists of biclustering image segments and annotation words. Given the query words, it is possible to select the image segment clusters that have strongest cooccurrence with the corresponding word clusters. These image segment clusters act as the selected segments relevant to a query. We rank text hits by our own tf.idf-based information retrieval system and image similarities by using a 20-dimensional vector describing the visual content of an image segment. Relevant image segments were selected by the biclustering procedure. Images were segmented by graph-based segmentation. We used neither query expansion nor relevance feedback; queries were generated automatically from the title and the description words. The later were weighted by 0.1.
This work was supported by a Yahoo! Faculty Research Grant and by grants MOLINGV NKFP-2/0024/2005, NKFP-2004 project Language Miner.
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Benczúr, A., Bíró, I., Brendel, M., Csalogány, K., Daróczy, B., Siklósi, D. (2008). Multimodal Retrieval by Text–Segment Biclustering. In: Peters, C., et al. Advances in Multilingual and Multimodal Information Retrieval. CLEF 2007. Lecture Notes in Computer Science, vol 5152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85760-0_64
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DOI: https://doi.org/10.1007/978-3-540-85760-0_64
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