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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© 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
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