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MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multimodal concept-dependent active learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
On the detection of semantic concepts at TRECVID
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Surrogate scoring for improved metasearch precision
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Extreme video retrieval: joint maximization of human and computer performance
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Image annotation refinement using random walk with restarts
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EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking
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Proceedings of the 6th ACM international conference on Image and video retrieval
Kodak's consumer video benchmark data set: concept definition and annotation
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LabelMe: A Database and Web-Based Tool for Image Annotation
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CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
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Proceedings of the 2008 ACM conference on Recommender systems
MM '08 Proceedings of the 16th ACM international conference on Multimedia
SheepDog: group and tag recommendation for flickr photos by automatic search-based learning
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Proceedings of the 18th international conference on World wide web
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KissKissBan: a competitive human computation game for image annotation
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Signal Processing
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.