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VideoSkip: event detection in social web videos with an implicit user heuristic

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Abstract

In this paper, we present a user-based event detection method for social web videos. Previous research in event detection has focused on content-based techniques, such as pattern recognition algorithms that attempt to understand the contents of a video. There are few user-centric approaches that have considered either search keywords, or external data such as comments, tags, and annotations. Moreover, some of the user-centric approaches imposed an extra effort to the users in order to capture required information. In this research, we are describing a method for the analysis of implicit users’ interactions with a web video player, such as pause, play, and thirty-seconds skip or rewind. The results of our experiments indicated that even the simple user heuristic of local maxima might effectively detect the same video-events, as indicated manually. Notably, the proposed technique was more accurate in the detection of events that have a short duration, because those events motivated increased user interaction in video hot-spots. The findings of this research provide evidence that we might be able to infer semantics about a piece of unstructured data just from the way people actually use it.

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Notes

  1. http://www.youtube.com/watch?v=GOQfIXxbjlE

  2. http://www.youtube.com/watch?v=Z09ythJT9Wk

  3. http://www.youtube.com/watch?v=LzkYvtqlT5I

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Acknowledgements

We are thankful to Ioannis Leftheriotis for his work on early system prototypes and to the participants of the user study.

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  1. Department of Informatics, Ionian University, Corfu, Greece

    Chrysoula Gkonela & Konstantinos Chorianopoulos

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  1. Chrysoula Gkonela
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  2. Konstantinos Chorianopoulos
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Correspondence to Konstantinos Chorianopoulos.

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Gkonela, C., Chorianopoulos, K. VideoSkip: event detection in social web videos with an implicit user heuristic. Multimed Tools Appl 69, 383–396 (2014). https://doi.org/10.1007/s11042-012-1016-1

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