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Publication details

Web-scale System for Image Similarity Search: When the Dreams Are Coming True

Basic information
Original title:	Web-scale System for Image Similarity Search: When the Dreams Are Coming True
Authors:	David Novák, Michal Batko, Pavel Zezula

Further information
Citation:	NOVÁK, David - BATKO, Michal - ZEZULA, Pavel. Web-scale System for Image Similarity Search: When the Dreams Are Coming True. In Proceedings of the Sixth International Workshop on Content-Based Multimedia Indexing (CBMI 2008). London : IEEE, 2008. ISBN 978-1-4244-2043-8, pp. 446-453. 18.6.2008, London, UK.
Original language:	English
Field:	Informatika
WWW:	http://index.ieeexplore.ieee.org/iel5/4558154/4564912/04564981.pdf
Type:	Article in Proceedings
Keywords:	similarity search; content-based search; image search; large-scale search; distributed data structures

Digital images have become a commodity which is searched on the Web as ordinarily as web pages. However, current large-scale engines search the images only on the basis of their annotations, while the content-based similarity systems do not seem to be ready for such scales. In this paper, we open the way to Web-scale image similarity search. We present a flexible system based on the metric space model and on the peer-to-peer paradigm. It uses M-Chord and M-Tree structures as its fundamental components and measures the image similarity by a combination of five MPEG-7 features. The system has been implemented including a graphical interface for online demonstrations and it currently indexes 10 million images crawled from the Web. We propose a novel strategy for approximate evaluation of similarity queries and we test its performance by a series of experiments. The results show that the system provides high-quality answers with response times around 0.5 second.

Related projects:

• Integrated approach to education of PhD students in the area of parallel and distributed systems
• Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)
• Complex similarity searching in very large data collections