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Key frame extraction is an important technique in video summarization, browsing, searching and understanding.
Based on the clustering concept, a key frame extraction algorithm specific to the GPU-based rendering is proposed, which significantly reduces the data size to satisfy the GPU memory requirement.
Our method for key frame extraction combines color histogram of the frames with the entropy of motion vectors.
However, in their approach, they used fixed write and check rates, which may affect to the detection accuracy if any failure happens in sampled frame extraction or matching.
Zhao et al. [13] studied the key techniques of pornographic image/video recognition algorithms, such as skin detection, key frame extraction, and classifier design in the compressed domain.
For a targeted metabolite analysis, the method 'peak alignment and frame extraction' is applied.
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In this section, we briefly introduce the main concepts behind key-frame extraction methods for 2D video and describe key-frame extraction methods for 3D video.
A robust real-time algorithm for key-frame extraction is presented.
The most relevant key-frame extraction methods are presented in Section 1.5.
Gong and Liu [52] proposed a key-frame extraction method based on SVD.
From all 3D key-frame extraction methods reviewed, only [16, 17, 25] used subjective evaluations.
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