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A new pyramidal opponent color-shape model based video shot boundary detection
A new pyramidal opponent color-shape model based video shot boundary detection-2020 Video shot boundary detection (VSBD) is one of the most essential criteria for
many intelligent video analysis-related applications, such as video retrieval,
indexing, browsing, categorization and summarization. VSBD aims to segment
big video data into meaningful fragments known as shots. This paper put forwards
a new pyramidal opponent colour-shape (POCS) model which can detect abrupt
transition (AT) and gradual transition (GT) simultaneously, even in the presence
of illumination changes, huge object movement between frames, and fast camera
motion. First, the content of frames in the video subjected to VSBD is represented
by the proposed POCS model. Consequently, the temporal nature of the POCS
model is subjected to a suitable segment (SS) selection procedure in order to
minimize the complexity of VSBD method. The SS from the video frames is
examined for transitions within it using a bagged-trees classifier (BTC) learned
on a balanced training set via parallel processing. To prove the superiority of the
proposed VSBD algorithm, it is evaluated on the TRECVID 2001,
TRECVID2007 and VIDEOSEG2004 data sets for classifying the basic units of
video according to no transition (NT), AT and GT. The experimental evaluation
results in an F1-score of 95.13%, 98.13% and 97.11% on the TRECVID 2001,
TRECVID2007 and VIDEOSEG2004 data sets, respectively. Keywords: Shot Boundary Detection | Abrupt Transition | Gradual Transition | Opponent Color space | Ensemble Algorithm |
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