Multi-Modal Visual Features-Based Video Shot Boundary Detection
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Publication Details
Author list: Tippaya S., Sitjongsataporn S., Tan T., Khan M.M., Chamnongthai K.
Publisher: Institute of Electrical and Electronics Engineers
Publication year: 2017
Journal: IEEE Access (2169-3536)
Volume number: 5
Start page: 12563
End page: 12575
Number of pages: 13
ISSN: 2169-3536
eISSN: 2169-3536
Languages: English-Great Britain (EN-GB)
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Abstract
One of the essential pre-processing steps of semantic video analysis is the video shot boundary detection (SBD). It is the primary step to segment the sequence of video frames into shots. Many SBD systems using supervised learning have been proposed for years; however, the training process still remains its principal limitation. In this paper, a multi-modal visual features-based SBD framework is employed that aims to analyze the behaviors of visual representation in terms of the discontinuity signal. We adopt a candidate segment selection that performs without the threshold calculation but uses the cumulative moving average of the discontinuity signal to identify the position of shot boundaries and neglect the non-boundary video frames. The transition detection is structurally performed to distinguish candidate segment into a cut transition and a gradual transition, including fade in/out and logo occurrence. Experimental results are evaluated using the golf video clips and the TREC2001 documentary video data set. Results show that the proposed SBD framework can achieve good accuracy in both types of video data set compared with other proposed SBD methods. ฉ 2013 IEEE.
Keywords
Cut transition detection, gradual transition detection, logo transition detection, transition pattern analysis
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