Detection and Feature Extraction of Collective Activity in Human-Computer Interaction
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 3
Abstract
Time-based online media, such as video, has been growing in importance. Still, there is limited research on information retrieval of time-coded media content. This work elaborates on the idea of extracting feature characteristics from time-based online content by means of users' interactions analysis instead of analyzing the content itself. Accordingly, a time series of users’ activity in online media is constructed and shown to exhibit rich temporal dynamics. Additionally it is demonstrated that is also possible to detect characteristic patterns in collective activity while accessing time-based media. Pattern detection of collective activity, as well as feature extraction of the corresponding pattern, is achieved by means of a time series clustering approach. This is demonstrated with the proposed approach featuring information-rich videos. It is shown that the proposed probabilistic algorithm effectively detects distinct shapes of the users’ time series, predicting correctly popularity dynamics, as well as their scale characteristics.
Authors and Affiliations
Ioannis Karydis, Markos Avlonitis, Phivos Mylonas, Spyros Sioutas
Keywords
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- EP ID EP95984
- DOI 10.14569/IJACSA.2016.070308
- Views 108
- Downloads 0
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