Automatic tagging by exploring tag information capability and correlation

Zhang, Xiaoming, Huang, Zi, Shen, Heng Tao, Yang, Yang and Li, Zhoujun (2012) Automatic tagging by exploring tag information capability and correlation. World Wide Web, 15 3: 233-256. doi:10.1007/s11280-011-0132-6

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Author Zhang, Xiaoming
Huang, Zi
Shen, Heng Tao
Yang, Yang
Li, Zhoujun
Title Automatic tagging by exploring tag information capability and correlation
Journal name World Wide Web   Check publisher's open access policy
ISSN 1386-145X
1573-1413
Publication date 2012-05
Year available 2011
Sub-type Article (original research)
DOI 10.1007/s11280-011-0132-6
Volume 15
Issue 3
Start page 233
End page 256
Total pages 24
Place of publication New York, NY, U.S.A.
Publisher Springer New York
Collection year 2012
Language eng
Formatted abstract
Automatic tagging can automatically label images and videos with semantic tags to significantly facilitate multimedia search and organization. However, most of existing tagging algorithms often don't differentiate between tags used to describe visual content, and neglect the semantic correlation of the assigned tag set. In this paper, we propose a novel automatic tagging algorithm which tags a test image or video with an Informative and Correlative Tag (ICTag) set. The assigned ICTag set can provide a more precise description of the multimedia object by exploring both the information capability of individual tags and the tag-to-set correlation. Measures to effectively estimate the information capability of individual tags and the correlation between a tag and the candidate tag set are designed. To reduce the computational complexity, we also introduce a heuristic method to achieve efficient automatic tagging. We conduct extensive experiments on the NUS-WIDE web image dataset downloaded from Flickr and the MCG-WEBV web video dataset downloaded from YouTube. The results confirm the efficiency and effectiveness of our proposed algorithm.
Keyword Automatic tagging
Information capability
Set correlation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 2 June 2011

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 11 times in Scopus Article | Citations
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Created: Mon, 06 Jun 2011, 09:09:06 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering