Cart (Loading....) | Create Account
Close category search window
 

Learn to Personalized Image Search From the Photo Sharing Websites

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
$31 $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)
Jitao Sang ; Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China ; Changsheng Xu ; Dongyuan Lu

Increasingly developed social sharing websites like Flickr and Youtube allow users to create, share, annotate, and comment medias. The large-scale user-generated metadata not only facilitate users in sharing and organizing multimedia content, but provide useful information to improve media retrieval and management. Personalized search serves as one of such examples where the web search experience is improved by generating the returned list according to the modified user search intents. In this paper, we exploit the social annotations and propose a novel framework simultaneously considering the user and query relevance to learn to personalized image search. The basic premise is to embed the user preference and query-related search intent into user-specific topic spaces. Since the users' original annotation is too sparse for topic modeling, we need to enrich users' annotation pool before user-specific topic spaces construction. The proposed framework contains two components: (1) a ranking-based multicorrelation tensor factorization model is proposed to perform annotation prediction, which is considered as users' potential annotations for the images; (2) we introduce user-specific topic modeling to map the query relevance and user preference into the same user-specific topic space. For performance evaluation, two resources involved with users' social activities are employed. Experiments on a large-scale Flickr dataset demonstrate the effectiveness of the proposed method.

Published in:

Multimedia, IEEE Transactions on  (Volume:14 ,  Issue: 4 )

Date of Publication:

Aug. 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.