Publication details


Similarity Query Postprocessing by Ranking

Basic information
Original title:Similarity Query Postprocessing by Ranking
Authors:Petra Budíková, Michal Batko, Pavel Zezula
Further information
Citation:BUDÍKOVÁ, Petra, Michal BATKO a Pavel ZEZULA. Similarity Query Postprocessing by Ranking. In M. Detyniecki, P. Knees, A. Nurnberger, M. Schedl, and S. Stober. Adaptive Multimedia Retrieval. Context, Exploration, and Fusion, LNCS 6817. Revised Selected Papers. Berlin: Springer-Verlag, 2012. s. 159-173, 15 s. ISBN 978-3-642-27168-7. doi:10.1007/978-3-642-27169-4_12.Export BibTeX
author = {Budíková, Petra and Batko, Michal and Zezula, Pavel},
address = {Berlin},
booktitle = {Adaptive Multimedia Retrieval. Context, Exploration, and Fusion, LNCS 6817},
doi = {},
edition = {Revised Selected Papers},
editor = {M. Detyniecki, P. Knees, A. Nurnberger, M. Schedl, and S. Stober},
keywords = {ranking; content-based retrieval; metric space},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Berlin},
isbn = {978-3-642-27168-7},
pages = {159-173},
publisher = {Springer-Verlag},
title = {Similarity Query Postprocessing by Ranking},
year = {2012}
Original language:English
Type:Article in Proceedings
Keywords:ranking; content-based retrieval; metric space

Current multimedia search technology is, especially in commercial applications, heavily based on text annotations. However, there are many applications such as image hosting web sites (e.g. Flickr or Picasa) where the text metadata are of poor quality in general. Searching such collections only by text gives usually rather unsatisfactory results. On the other hand, multimedia retrieval systems based purely on content can retrieve visually similar results but lag behind with the ability to grasp the semantics expressed by text annotations. In this paper, we propose various ranking techniques that can be transparently applied on any content-based retrieval system in order to improve the search results quality and user satisfaction. We demonstrate the usefulness of the approach on two large real-life datasets indexed by the MUFIN system. The improvement of the ranked results was evaluated by real users using an online survey.

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