Searching 100M Images by Content Similarity.
Paolo Bolettieri, Fabrizio Falchi, Claudio Lucchese, Yosi Mass, Raffaele Perego, Fausto Rabitti, Michal Shmueli-Scheuer · 2009
Abstract. In this paper we present the web user interface of a scalable and distributed system for image retrieval based on visual features and annotated text, developed in the context of the SAPIR project. Its ar-chitecture makes use of Peer-to-Peer networks to achieve scalability and efficiency allowing the management of huge amount of data and simulta-neous access by a large number of users. Describing the SAPIR web user interface we want to encourage final users to use SAPIR to search by content similarity, together with the usual text search, on a large image collection (100 million images crawled from Flickr) with realistic response time. On the ground of the statistics collected, it will be possible, for the first time, to study the user behavior (e.g., the way they combine text and image content search) in this new realistic environment. 1 Introduction: the SAPIR Project Non-text data, such as images, music, animations, and videos is nowadays a large component of the Web. However, web tools for performing image searching, as