Automatic and online setting of similarity thresholds in content-based visual information retrieval problems.
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Date
2016
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Abstract
Several information recovery systems use functions to determine similarity among objects in a collection. Such
functions require a similarity threshold, from which it becomes possible to decide on the similarity between two given
objects. Thus, depending on its value, the results returned by systems in a search may be satisfactory or not. However,
the definition of similarity thresholds is difficult because it depends on several factors. Typically, specialists fix a
threshold value for a given system, which is used in all searches. However, an expert-defined value is quite costly and
not always possible. Therefore, this study proposes an approach for automatic and online estimation of the similarity
threshold value, to be specifically used by content-based visual information retrieval system (image and video) search
engines. The experimental results obtained with the proposed approach prove rather promising. For example, for one
of the case studies, the performance of the proposed approach achieved 99.5 % efficiency in comparison with that
obtained by a specialist using an empirical similarity threshold. Moreover, such automated approach becomes more
scalable and less costly.
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Keywords
Content-based retrieval systems
Citation
BESSAS, I. L. et al. Automatic and online setting of similarity thresholds in content-based visual information retrieval problems. EURASIP Journal on Advances in Signal Processing, v. 2016, n. 32, p. 1-16, 2016. Disponível em: <http://download.springer.com/static/pdf/29/art%253A10.1186%252Fs13634-016-0324-4.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Farticle%2F10.1186%2Fs13634-016-0324-4&token2=exp=1484910369~acl=%2Fstatic%2Fpdf%2F29%2Fart%25253A10.1186%25252Fs13634-016-0324-4.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Farticle%252F10.1186%252Fs13634-016-0324-4*~hmac=095fb19f10096bbaa22ab30d86e5bc922726ab9dd235363e092133d48ce3df16>. Acesso em: 20 jan. 2017.