Building a web-scale image similarity search system

As the number of digital images is growing fast and Content-based Image Retrieval (CBIR) is gaining in popularity, CBIR systems should leap towards Webscale datasets. In this paper, we report on our experience in building an experimental similarity search system on a test collection of more than 50 million images. The first big challenge we have been facing was obtaining a collection of images of this scale with the corresponding descriptive features. We have tackled the non-trivial process of image crawling and extraction of several MPEG-7 descriptors. The result of this effort is a test collection, the first of such scale, opened to the research community for experiments and comparisons. The second challenge was to develop indexing and searching mechanisms able to scale to the target size and to answer similarity queries in real-time. We have achieved this goal by creating sophisticated centralized and distributed structures based purely on the metric space model of data. We have ...

• Type: Journal
• Year: 2010
• Where: MTA
• Authors: Michal Batko, Fabrizio Falchi, Claudio Lucchese, David Novak, Raffaele Perego, Fausto Rabitti, Jan Sedmidubský, Pavel Zezula
• Keywords: Content-based Image Retrieval, Image, MTA 2010, Similarity Search