By Topic

Hamming DHT: Taming the similarity search

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

4 Author(s)
Rodolfo da Silva Villaca ; School of Electrical and Computer Engineering (FEEC/UNICAMP), Campinas - SP - Brazil ; Luciano Bernardes de Paula ; Rafael Pasquini ; MaurĂ­cio Ferreira Magalhaes

The semantic meaning of a content is frequently represented by content vectors in which each dimension represents an attribute of this content, such as, keywords in a text, colors in a picture or profile information in a social network. However, one important challenge in this semantic context is the storage and retrieval of similar contents, such as the search for similar images assisting a medical procedure. Based on it, this paper presents a new Distributed Hash Table (DHT), called Hamming DHT, in which Locality Sensitive Hashing (LSH) functions, specially the Random Hyperplane Hashing (RHH), are used to generate content identifiers, propitiating a scenario in which similar contents are stored in peers nearly located in the indexing space of the proposed DHT. The evaluations of this work simulate profiles in a social network to verify if the proposed DHT is capable of reducing the number of hops required in order to improve the recall in the context of a similarity search.

Published in:

2013 IEEE 10th Consumer Communications and Networking Conference (CCNC)

Date of Conference:

11-14 Jan. 2013