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Peer-to-peer networking offers a scalable solution for sharing multimedia data across the network. With a large amount of visual data distributed among different nodes, it is an important but challenging issue to perform content-based retrieval in peer-to-peer networks. While most of the existing methods focus on indexing high dimensional visual features and have limitations of scalability, in this...
Recently, the Bag-of-Features (BoF) model has emerged as a popular solution to scalable content-based image retrieval (CBIR), due to great success of the Bag-of-Words (BoW) model in textual information processing. While most of the existing algorithms on CBIR in P2P networks focus on indexing high dimensional low level features, we propose to address such an issue by employing the BoF model. However,...
Known by its exceptional scalability and flexibility, Peer-to-peer (P2P) technique is arguably one of the most important mechanisms for sharing massive data (e.g. media data). It has been challenging to support similarity search in structured P2P networks, though it provides efficient indexing for exact search. In this paper, we present an efficient indexing technique to support complex similarity...
Supporting complex and efficient lookup queries in peer-to-peer networks is challenging, though simple keyword based lookup queries are well supported by most deployed systems. This paper presents a two-level indexing structure built on distributed hash table (DHT) aiming to support range queries on high-dimensional feature space in peer-to-peer network. Unlike most existing systems, where every node...
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