Research on Video Content Recognition Based on Clustering Algorithm

Computer & Telecommunication

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CHEN Shuang-quan

Wuhan Institute of Shipbuilding Technology

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Abstract In this paper, we study the rules of video website content monitoring, and improve the existing K-MEANS clustering algorithm according to the new rule. The K-MEANS clustering algorithm has obvious defects in clustering recognition and computing efficiency due to the influence of unstructured factors of the video content data. The advantage of the improved K-MEANS clustering algorithm takes into account the sequence relations of video frames and maintains the acceptable retrieval speed.

Key words： data mining video content recognition clustering analysis K-MEANS clustering algorithm

ZTFLH:

TP391.41

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CHEN Shuang-quan. Research on Video Content Recognition Based on Clustering Algorithm. Computer & Telecommunication, 2017, 1(11): 44-46.

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http://www.computertelecom.com.cn/EN/ OR http://www.computertelecom.com.cn/EN/Y2017/V1/I11/44

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