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Clipboard: A Visual Search and Browsing Engine for Tablet and PC

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Advances in Multimedia Modeling (MMM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7131))

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Abstract

In this work, we present a handheld video browser that utilizes two methods of search; Concept Search and Keyframe Similarity. Concept Search allows a user to define a query using selected visual concepts and presents the user with a cluster of video segments based on extracted image features using OpponentSIFT. Keyframe Similarity has a dependance on the previous search for input criteria, allowing a user to select a keyframe for similarity search, returning three types of results; local keyframes from the current scene, global shot similarity based on visual features and text similarity of shots, based on frequently occurring words generated from ASR transcripts.

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References

  1. van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating Color Descriptors for Object and Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)

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© 2012 Springer-Verlag Berlin Heidelberg

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Scott, D., Guo, J., Wang, H., Yang, Y., Hopfgartner, F., Gurrin, C. (2012). Clipboard: A Visual Search and Browsing Engine for Tablet and PC. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_65

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  • DOI: https://doi.org/10.1007/978-3-642-27355-1_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27354-4

  • Online ISBN: 978-3-642-27355-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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