By Topic

Efficient high-dimensional retrieval in structured P2P networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Lelin Zhang ; School of Information Technologies, University of Sydney, NSW 2006 Australia ; Zhiyong Wang ; Dagan Feng

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.

Published in:

Multimedia and Expo (ICME), 2010 IEEE International Conference on

Date of Conference:

19-23 July 2010