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Title: Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
Authors: Wang, M. 
Ni, B.
Hua, X.-S.
Chua, T.-S. 
Keywords: Annotation
Interactive tagging
Tag location
Tag recommendation
Tag refinement
Issue Date: 2012
Citation: Wang, M., Ni, B., Hua, X.-S., Chua, T.-S. (2012). Assistive tagging: A survey of multimedia tagging with human-computer joint exploration. ACM Computing Surveys 44 (4). ScholarBank@NUS Repository.
Abstract: Along with the explosive growth of multimedia data, automatic multimedia tagging has attracted great interest of various research communities, such as computer vision, multimedia, and information retrieval. However, despite the great progress achieved in the past two decades, automatic tagging technologies still can hardly achieve satisfactory performance on real-world multimedia data that vary widely in genre, quality, and content. Meanwhile, the power of human intelligence has been fully demonstrated in the Web 2.0 era. If well motivated, Internet users are able to tag a large amount of multimedia data. Therefore, a set of new techniques has been developed by combining humans and computers for more accurate and efficient multimedia tagging, such as batch tagging, active tagging, tag recommendation, and tag refinement. These techniques are able to accomplish multimedia tagging by jointly exploring humans and computers in different ways. This article refers to them collectively as assistive tagging and conducts a comprehensive survey of existing research efforts on this theme. We first introduce the status of automatic tagging and manual tagging and then state why assistive tagging can be a good solution. We categorize existing assistive tagging techniques into three paradigms: (1) tagging with data selection&organization; (2) tag recommendation; and (3) tag processing. We introduce the research efforts on each paradigm and summarize the methodologies. We also provide a discussion on several future trends in this research direction. © 2012 ACM.
Source Title: ACM Computing Surveys
ISSN: 03600300
DOI: 10.1145/2333112.2333120
Appears in Collections:Staff Publications

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