Paper Info
Title
SAPIR: Scalable and Distributed Image Searching
Abstract
In this paper we present a scalable and distributed system for image retrieval based on visual features and annotated text. This system is the core of the SAPIR project. Its architecture makes use of Peer-to-Peer networks to achieve scalability and efficiency allowing the management of huge amount of data. For the presented demo we use 10 million images and accom- panying text (tags, comments, etc.) taken from Flickr. Through the web interface it is possible to efficient perform content- based similarity search, as well as traditional text search on the metadata annotated by the Flickr community. Fast complex query processing is also possible combining visual features and text. We show that the combination of content-based and text search on a large scale can dramatically improve the capability of a multimedia search system to answer the users needs and that the Peer-to-Peer based architecture can cope with the scalability issues (response time obtained for this demo over 10 million images is always below 500 milliseconds). Index Terms—Peer-to-Peer, metric spaces, distributed, scala- bility, MPEG-7, similarity search. I. INTRODUCTION 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 (such the ones provided by Google, Yahoo!, or MSN Live Search) simply index the text associated with the image. Web search is dominated today by text only indexes enriched by page rank algorithms, thus search for audio-visual content, it is limited to associated text and metadata annotations. Image indexing methods based on content-based analysis or pattern matching (which typically analyze the characteristics of images, i.e., features, such as colors and shapes) are usually not exploited at all. In fact, for this kind of data the appro- priate search methods are based on similarity paradigms that typically exploits range queries and nearest neighbor queries. These queries are computationally more intensive than text search, because conventional inverted indexes used for text are not applicable for such data. The European project SAPIR (Search on Audio-visual content using Peer-to-peer Information Retrieval)1 aims at breaking this technological barrier by developing a large- scale, distributed Peer-to-Peer infrastructure that will make it possible to search for audio-visual content by querying the specific characteristics (i.e., features) of the content. SAPIR's goal is to establish a giant Peer-to-Peer network, where users 1http://www.sapir.eu/ are peers that produce audiovisual content using multiple devices (e.g., cell phones) and service providers will use more powerful peers that maintain indexes and provide search capabilities "A picture is worth a thousand words" so using an image taken by a cell phone to find information about e.g. a monu- ment we bump into or singing a melody as a search hint for a full song, combined with optional metadata annotations and user and social networking context will provide the next level of search capabilities and precision of retrieved results.
Year
Venue
Keywords
2007
SAMT (Posters and Demos)
distributed system,pattern matching,service provider,metric space,indexing terms,web interface,inverted index,indexation,range query,image retrieval,social network,similarity search,information retrieval
Field
DocType
Citations 
Metadata,Architecture,Multimedia search,Information retrieval,Computer science,Full text search,Image retrieval,Response time,Nearest neighbor search,Scalability
Conference
4
PageRank 
References 
Authors
0.57
9
5
Name
Order
Citations
PageRank
Fabrizio Falchi145955.65
Mouna Kacimi225719.82
Yosi Mass357460.91
Fausto Rabitti4766193.92
Pavel Zezula52113396.39