DocumentCode :
2283979
Title :
Efficient high-dimensional retrieval in structured P2P networks
Author :
Zhang, Lelin ; Wang, Zhiyong ; Feng, Dagan
Author_Institution :
Sch. of Inf. Technol., Univ. of Sydney, Sydney, NSW, Australia
fYear :
2010
fDate :
19-23 July 2010
Firstpage :
1439
Lastpage :
1444
Abstract :
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 retrieval on high-dimensional data by improving existing approach Multi-dimensional Rectangulation with Kd-trees (MURK). In order to make search more user-centric, relevance feedback techniques are also investigated. To the best of our knowledge, it is the first attempt of utilizing relevance feedback in structured P2P networks. Simulations for content based music retrieval with multiple acoustic features have been conducted to investigate the properties and efficiency of the proposed approach.
Keywords :
indexing; peer-to-peer computing; relevance feedback; search problems; trees (mathematics); Kd-tree; P2P network; complex similarity retrieval; indexing technique; multidimensional rectangulation; peer-to-peer; relevance feedback; similarity search; user-centric search technique; Indexing; Load management; Mel frequency cepstral coefficient; Music; Peer to peer computing; Radio frequency; Routing; High dimensional indexing and retrieval; Music Information Retrieval; Relevance feedback; Structured Peer-to-Peer networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo (ICME), 2010 IEEE International Conference on
Conference_Location :
Suntec City
ISSN :
1945-7871
Print_ISBN :
978-1-4244-7491-2
Type :
conf
DOI :
10.1109/ICME.2010.5582934
Filename :
5582934
Link To Document :
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