Image tagging with social assistance

Yang, Yang, Gao, Yue, Zhang, Hanwang, Shao, Jie and Chua, Tat-Seng (2014). Image tagging with social assistance. In: ICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014. 2014 4th ACM International Conference on Multimedia Retrieval, ICMR 2014, Glasgow, United Kingdom, (81-88). 1-4 April 2014. doi:10.1145/2578726.2578731

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Author Yang, Yang
Gao, Yue
Zhang, Hanwang
Shao, Jie
Chua, Tat-Seng
Title of paper Image tagging with social assistance
Conference name 2014 4th ACM International Conference on Multimedia Retrieval, ICMR 2014
Conference location Glasgow, United Kingdom
Conference dates 1-4 April 2014
Convener Joemon Jose
Proceedings title ICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014
Place of Publication New York, NY, United States
Publisher Association for Computing Machinery
Publication Year 2014
Sub-type Fully published paper
DOI 10.1145/2578726.2578731
Open Access Status
ISBN 9781450327824
Start page 81
End page 88
Total pages 8
Collection year 2015
Language eng
Formatted Abstract/Summary
Image tagging, also known as image annotation and image conception detection, has been extensively studied in the literature. However, most existing approaches can hardly achieve satisfactory performance owing to the deficiency and unreliability of the manually-labeled training data. In this paper, we propose a new image tagging scheme, termed social assisted media tagging (SAMT), which leverages the abundant user-generated images and the associated tags as the "social assistance" to learn the classifiers. We focus on addressing the following major challenges: (a) the noisy tags associated to the web images; and (b) the desirable robustness of the tagging model. We present a joint image tagging framework which simultaneously refines the erroneous tags of the web images as well as learns the reliable image classifiers. In particular, we devise a novel tag refinement module for identifying and eliminating the noisy tags by substantially exploring and preserving the low-rank nature of the tag matrix and the structured sparse property of the tag errors. We develop a robust image tagging module based on the 𝓁2,𝑝-norm for learning the reliable image classifiers. The correlation of the two modules is well explored within the joint framework to reinforce each other. Extensive experiments on two real-world social image databases illustrate the superiority of the proposed approach as compared to the existing methods.
Keyword Image tagging
Low rank
Sparse coding
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

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