Document Type
Conference Paper
Department/Unit
Department of Computer Science
Title
Collaborative and content-based image labeling
Language
English
Abstract
Many on-line photo sharing systems allow users to tag their images so as to support semantic image search. In this paper, we study how one can take advantages of the already-tagged images to (semi-)automate the labeling of newly uploaded ones. In particular, we propose a hybrid approach for the prediction where user-provided tags and image visual contents are fused under a unified probabilistic framework. Kernel smoothing and collaborative filtering techniques are explored for improving the accuracy of the probabilistic models estimation. By comparing with some state-of-the-art content-based image labeling methods, we have empirically shown that 1) the proposed method can achieve comparable tag prediction accuracy when there is no user-provided tag, and that 2) it can significantly boost the prediction accuracy if the user can provide just a few tags.
Keywords
Collaboration, Labeling, Filtering, Image retrieval, Computer science, Accuracy, Kernel, Smoothing methods, Content based retrieval, Tagging
Publication Date
12-2008
Source Publication Title
19th International Conference on Pattern Recognition, 2008 ICPR 2008 ; 8 - 11 Dec. 2008, Tampa, Florida, USA
Editors
International Association for Pattern Recognition
Publisher
Tampa, United States
Peer Reviewed
1
Copyright
©2008 IEEE
Funder
This work was supported in part by MoE research Fund under contract 104075, Shanghai Municipal R&D Foundation under contract 06DZ15008, and MoST Support Program under contract 2007BAH09B03.
DOI
10.1109/ICPR.2008.4761473
Link to Publisher's Edition
ISSN (print)
10514651
ISBN (print)
9781424421749
ISBN (electronic)
9781424421756
Recommended Citation
Zhou, Ning, William K. Cheung, Xiangyang Xue, and Guoping Qiu. "Collaborative and content-based image labeling." 19th International Conference on Pattern Recognition, 2008 ICPR 2008 ; 8 - 11 Dec. 2008, Tampa, Florida, USA (2008).