Method and device for clustering video files

Abstract

The embodiment of the invention provides a clustering method for video files. The clustering method includes the steps of obtaining a luminance mean value curve of the video files according to luminance mean values of images in the video files, obtaining a characteristic curve of the video files according to extreme points in the luminance mean value curve, and carrying out clustering on the video files according to the characteristic curve of the video files. The embodiment of the invention further discloses a clustering device for the video files. By the adoption of the clustering method and device for the video files, clustering is carried out on the video files according to the content characteristics of the video files, extraction of a large number of video frames and the huge calculation amount needed for clustering of videos in the prior art are avoided, and the efficiency of video clustering is improved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a video file clustering method and device. Background technique [0002] With the rapid development of multimedia technology and the maturity of modern computer technology, especially mass data storage and transmission technology, video, as a main media type, has increasingly become an indispensable information carrier in people's life, education, entertainment and other aspects. With the rapid development of video compression technology, the decreasing price of storage devices and the increasing speed of network transmission, more and more video data are available to users. Although video has the advantages of strong expressiveness, large amount of information, and vivid images, but at the same time, its unstructured data format, huge data volume, and opaque content make the management and analysis of video data such as video Data clustering and retrieval are quite difficult....

AI Technical Summary

Problems solved by technology

There are two main problems in this method: (1) Too many frames are usually selected as key frames in order not to miss key frames, resulting in redundant key frames, which leads to the need to store intermediate information and The amount of calculation is very large, and the processing efficiency is very low in the face of massive video files; (2) The representativeness of the selected key frames is not strong, resulting in multiple selections and omissions when retrieving videos through key frames

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