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Publication details

Combining Metric Features in Large Collections

Further information
Citation:	BATKO, Michal - KOHOUTKOVÁ, Petra - ZEZULA, Pavel. Combining Metric Features in Large Collections. In 1st International Workshop on Similarity Search and Applications (SISAP 2008). Los Alamitos CA, Washington, Tokyo : IEEE Computer Society, 2008. ISBN 978-0-7695-3101-4, pp. 79-86. 11.4.2008, Cancun, Mexico.
Original language:	English
Field:	Informatika
WWW:	http://www.sisap.org/
Type:	Article in Proceedings
Keywords:	similarity search; complex query; p2p network; approximation

Current information systems are required to process complex digital objects, which are typically characterized by multiple descriptors. Since the values of many descriptors belong to non-sortable domains, they are effectively comparable only by a sort ofsimilarity. Moreover, the scalability is very important in the current digital-explosion age. Therefore, we propose a distributed extension of the well-known threshold algorithm for peer-to-peer paradigm. The technique allows to answer similarity queries that combine multiple similarity measures and due to its peer-to-peer nature it is highly scalable. We also explore possibilities of approximate evaluation strategies, where some relevant results can be lost in favor of increasing the efficiency by order of magnitude. To reveal the strengths and weaknesses of our approach we have experimented with a 1.6 million image database from Flicker comparing the content of the images by five similarity measures from the MPEG-7 standard. To the best of our knowledge, the experience with such a huge real-life dataset is quite unique.

Related projects:

• Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)
• Complex similarity searching in very large data collections