2014 CrowdsourcedTimeSyncVideoTaggin

• (Wu et al., 2014) ⇒ Bin Wu, Erheng Zhong, Ben Tan, Andrew Horner, and Qiang Yang. (2014). “Crowdsourced Time-sync Video Tagging Using Temporal and Personalized Topic Modeling.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623625

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Crowdsourced+Time-sync+Video+Tagging+Using+Temporal+and+Personalized+Topic+Modeling
• http://dl.acm.org/citation.cfm?id=2623330.2623625&preflayout=flat#citedby

Quotes

Author Keywords

• Crowdsourcing; data mining; temporal and personalized model; topic modeling; video tagging

Abstract

Time-sync video tagging aims to automatically generate tags for each video shot. It can improve the user's experience in previewing a video's timeline structure compared to traditional schemes that tag an entire video clip. In this paper, we propose a new application which extracts time-sync video tags by automatically exploiting crowdsourced comments from video websites such as Nico Nico Douga, where videos are commented on by online crowd users in a time-sync manner. The challenge of the proposed application is that users with bias interact with one another frequently and bring noise into the data, while the comments are too sparse to compensate for the noise. Previous techniques are unable to handle this task well as they consider video semantics independently, which may overfit the sparse comments in each shot and thus fail to provide accurate modeling. To resolve these issues, we propose a novel temporal and personalized topic model that jointly considers temporal dependencies between video semantics, users' interaction in commenting, and users' preferences as prior knowledge. Our proposed model shares knowledge across video shots via users to enrich the short comments, and peels off user interaction and user bias to solve the noisy-comment problem. Log-likelihood analyses and user studies on large datasets show that the proposed model outperforms several state-of-the-art baselines in video tagging quality. Case studies also demonstrate our model's capability of extracting tags from the crowdsourced short and noisy comments.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 CrowdsourcedTimeSyncVideoTaggin	Qiang Yang Erheng Zhong Bin Wu Ben Tan Andrew Horner			Crowdsourced Time-sync Video Tagging Using Temporal and Personalized Topic Modeling				10.1145/2623330.2623625		2014