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Mental Visual Browsing

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9517)

Abstract

We present a surprisingly easy-to-use video browser for helping users to pinpoint a specific video shot in mind, within a long video. At each interactive iteration, the only user effort required is to click 1 shot, which most visually relates to the user’s mental target, out of 8 displayed shots. Then, the system updates the browsing model and display another 8 shots for the next iteration. The proposed system is underpinned by a theoretically-sound Bayesian framework that maintains the probabilities of all the video shots segmented from the long video. This framework guarantees that we can find the target shot out of around 1-h video within 3–5 iterations. We believe that our system will perform well in the Video Broswer Showdown game of MMM 2016.

Keywords

  • Relevance feedback
  • Bayesian system
  • Video browsing
  • Mental search

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  • DOI: 10.1007/978-3-319-27674-8_44
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Correspondence to Xindi Shang .

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© 2016 Springer International Publishing Switzerland

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He, J., Shang, X., Zhang, H., Chua, TS. (2016). Mental Visual Browsing. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_44

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  • DOI: https://doi.org/10.1007/978-3-319-27674-8_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27673-1

  • Online ISBN: 978-3-319-27674-8

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