Conceptual image retrieval over a large scale database
|Conceptual image retrieval over a large scale database|
|Author(s)||Popescu A., Le Borgne H., Moellic P.-A.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Image retrieval, Large-scale database, Query reformulation (Extra: ImageCLEF, Large-scale database, Noisy image, Visual concept, Visual cues, Wikipedia, Wordnet, Database systems, Image analysis, Linguistics, Image retrieval)|
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Conceptual image retrieval over a large scale database is a 2009 conference paper written in English by Popescu A., Le Borgne H., Moellic P.-A. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Image retrieval in large-scale databases is currently based on a textual chains matching procedure. However, this approach requires an accurate annotation of images, which is not the case on the Web. To tackle this issue, we propose a reformulation method that reduces the influence of noisy image annotations. We extract a ranked list of related concepts for terms in the query from WordNet and Wikipedia, and use them to expand the initial query. Then some visual concepts are used to re-rank the results for queries containing, explicitly or implicitly, visual cues. First evaluations on a diversified corpus of 150000 images were convincing since the proposed system was ranked 4 th and 2 nd at the WikipediaMM task of the ImageCLEF 2008 campaign .
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