Abstract
Conventional Web image search engines can return reasonably accurate results for queries containing concrete terms, but the results are less accurate for queries containing only abstract terms, such as “spring” or “peace.” To improve the recall ratio without drastically degrading the precision ratio, we developed a method that replaces an abstract query term given by a user with a set of concrete terms and that uses these terms in queries input into Web image search engines. Concrete terms are found for a given abstract term by making use of social tagging information extracted from a social photo sharing system, such as Flickr. This information is rich in user impressions about the objects in the images. The extraction and replacement are done by (1) collecting social tags that include the abstract term, (2) clustering the tags in accordance with the term co-occurrence of images, (3) selecting concrete terms from the clusters by using WordNet, and (4) identifying sets of concrete terms that are associated with the target abstract term by using a technique for association rule mining. Experimental results show that our method improves the recall ratio of Web image searches.
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Kato, M., Ohshima, H., Oyama, S., Tanaka, K. (2008). Can Social Tagging Improve Web Image Search?. In: Bailey, J., Maier, D., Schewe, KD., Thalheim, B., Wang, X.S. (eds) Web Information Systems Engineering - WISE 2008. WISE 2008. Lecture Notes in Computer Science, vol 5175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85481-4_19
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DOI: https://doi.org/10.1007/978-3-540-85481-4_19
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