Abstract
We propose a generic diversity and two novel spatial diversity algorithms for (image) search result diversification. The outputs of the algorithms are compared with the standard search results (which contains no diversity implementation) and found to be promising. In particular, the geometric mean spatial diversity algorithm manages to achieve good geographical diversity while not significantly reducing precision. To the best of our knowledge, such a quantitive evaluation of spatial diversity algorithms for context based image retrieval is new to the community.
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Paramita, M.L., Tang, J., Sanderson, M. (2009). Generic and Spatial Approaches to Image Search Results Diversification. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_56
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DOI: https://doi.org/10.1007/978-3-642-00958-7_56
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