Video key frame extraction method based on shot boundary detection and clustering

Abstract

The invention discloses a video key frame extraction method based on shot boundary detection and clustering, comprising the following steps: S1, reading a video, and extracting the image features of each frame of video; S2, calculating the image feature difference between each frame of video and the adjacent precious frame of video; S3, detecting the shot boundary through use of a sliding window adaptive method; S4, extracting the key frame of each shot through use of a clustering algorithm; and S5, calculating the weight of each key frame, selecting a plurality of higher-weight key frames, sorting the key frames according to the chronological order, and taking the selected key frames as the key frames of the video. According to the invention, by comparing the image features of the image frames, the shot boundary is detected accurately, and the key frame of each shot is extracted efficiently. N higher-weight key frames are taken as the key frames of the video according to the chronological order, and the key frames can effectively represent the whole video.

Description

technical field [0001] The invention relates to the technical field of video processing, in particular to a video key frame extraction method based on shot boundary detection and clustering. Background technique [0002] With the rapid development of Internet multimedia, a large number of videos are uploaded to the Internet every day, and content-based video retrieval has become an urgent problem to be solved. Video is a data stream in which images are coded sequentially in chronological order. In the case of limited storage space and computing resources, the key frames of the video are extracted to represent the video, and the key frames are used to create an index, which provides the possibility for content-based video retrieval. . Therefore, key frame extraction technology is one of the key technologies to realize content-based video retrieval. [0003] In video, a sequence of temporally uninterrupted frames is called a shot. The key frame is to extract the static imag...

AI Technical Summary

Problems solved by technology

The method based on the shot boundary is not robust and is easily affected by the degree of video image changes, and the selected key frames are not necessarily representative

The method based on motion and content analysis is also not robust and has high computational complexity, which is not suitable for real-time scenarios

The clustering-based method has high space complexity and cannot effectively preserve the time sequence and dynamic information in the original shot, so it is suitable for short videos

The method based on compressed video does not need to fully decompress the video stream, and the computational complexity is low, but it does not effectively use the video image information, and the representativeness of the extracted key frames is poor.

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