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Earlier methods for video shot partitioning are described in [2 4, 16 18].
BoW (bag of words) is one of the most popular methods for video representation.
All these methods for video analysis suggest certain ways and sequences to strengthen the outcomes generated from complex video data.
Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection.
Li and Lai [28] described methods for video partitioning using motion estimation, where the motion vectors are extracted using optical flow computations.
Also, it has been observed that most of the existing methods for video quality assessment are limited to one encoder implementation or rather one particular setting of an encoder.
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All the state-of-the-art methods for videos dealt with a very simple kind of defect: long vertical dark (or bright) scratches, with constant color, width and orientation.
The current paper presents a new genetic algorithm (GA -based method for video seGA -basedn.
In this paper, a deep domain adaptation based method for video smoke detection is proposed to extract a powerful feature representation of smoke.
It is a deep learning method for video SR reconstruction, which considers not only the mapping relationship among associated low-resolution (LR) and high-resolution (HR) image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames.
(6) The use of such method for video completion tasks.
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