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Social Video Retrieval: Research Methods in Controlling, Sharing, and Editing of Web Video

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Book cover Social Media Retrieval

Part of the book series: Computer Communications and Networks ((CCN))

Abstract

Content-based video retrieval has been a very efficient technique with new video content, but it has not fully explored the increasingly dynamic interactions between users and content. We present a comprehensive survey on user-based techniques and instrumentation for social video retrieval researchers. Community-based approaches suggest there is much to learn about an unstructured video just by analyzing the dynamics of how it is being used. In particular, we explore three pillars of online user activity with video content: (1) Seeking patterns within a video is linked to interesting video segments, (2) sharing patterns between users indicates that there is a correlation between social activity and popularity of a video, and (3) editing of live events is automated through the synchronization of audio across multiple viewpoints of the same event. Moreover, we present three complementary research methods in social video retrieval: experimental replication of user activity data and signal analysis, data mining and prediction on natural user activity data, and hybrid techniques that combine robust content-based approaches with crowd sourcing of user-generated content. Finally, we suggest further research directions in the combination of richer user and content modeling, because it provides an attractive solution to the personalization, navigation, and social consumption of videos.

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Author information

Authors and Affiliations

  1. Ionian University, T. Triantafyli, 15125, Marousi, Greece

    Konstantinos Chorianopoulos

  2. Yahoo! Research, Santa Clara, CA, USA

    David A. Shamma & Lyndon Kennedy

Authors
  1. Konstantinos Chorianopoulos
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  2. David A. Shamma
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  3. Lyndon Kennedy
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Corresponding author

Correspondence to Konstantinos Chorianopoulos .

Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom

    Naeem Ramzan

  2. Director of Product Innovation, Search, Netflix, Winchester Circle 100, Los Gatos, 95032, California, USA

    Roelof van Zwol

  3. School of Integrated Technology, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon, 406-840, Korea, Republic of (South Korea)

    Jong-Seok Lee

  4. Institut für Telekommunikationssysteme, Technische Universität Berlin, Einsteinufer 17, Berlin, 10587, Germany

    Kai Clüver

  5. Media Computing Group, Microsoft Research, 555 108th Ave NE, Bellevue, 98004, Washington, USA

    Xian-Sheng Hua

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Chorianopoulos, K., Shamma, D.A., Kennedy, L. (2013). Social Video Retrieval: Research Methods in Controlling, Sharing, and Editing of Web Video. In: Ramzan, N., van Zwol, R., Lee, JS., Clüver, K., Hua, XS. (eds) Social Media Retrieval. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4555-4_1

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  • DOI: https://doi.org/10.1007/978-1-4471-4555-4_1

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