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International Conference on Multimedia Modeling

MMM 2015: MultiMedia Modeling pp 306–317Cite as

Improving Interactive Known-Item Search in Video with the Keyframe Navigation Tree

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8935))

Abstract

In this paper we propose the Keyframe Navigation Tree (KNT) as navigational aid in video for interactive search. The KNT is a hierarchical visualization of keyframes that can compactly represent the content of a video with different levels of details. It can be used as an alternative, or in addition, to a common seeker-bar of a video player. Through a user study with 20 participants we show that the proposed navigation approach not only allows significantly faster interactive search in video than a common video player, but also requires significantly less effort (also less mental and physical load) and is much more enjoyable to use.

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Author information

Authors and Affiliations

  1. Klagenfurt University, Universitaetsstr. 65-67, 9020, Klagenfurt, Austria

    Marco A. Hudelist & Klaus Schoeffmann

  2. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Qing Xu

Authors
  1. Marco A. Hudelist
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  2. Klaus Schoeffmann
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  3. Qing Xu
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Editor information

Editors and Affiliations

  1. University of Technology, P.O. Box 123, 2007, Sydney, NSW, Australia

    Xiangjian He , Dacheng Tao  & Muhammad Abul Hasan ,  & 

  2. University of Newcastle, University Dr, Callaghan, 2308, NSW, Australia

    Suhuai Luo

  3. National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95, Zhongguancun East Road, 100190, Beijing, P.R. China

    Changsheng Xu

  4. Shanghai Jitotong University, 800 Dong Chuan Rd, 200240, Shanghai, China

    Jie Yang

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© 2015 Springer International Publishing Switzerland

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Hudelist, M.A., Schoeffmann, K., Xu, Q. (2015). Improving Interactive Known-Item Search in Video with the Keyframe Navigation Tree. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_27

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  • DOI: https://doi.org/10.1007/978-3-319-14445-0_27

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14444-3

  • Online ISBN: 978-3-319-14445-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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