关键词: 视频数据质量, 近重复视频, 视频清洗, VGG-16深度网络, FD-means聚类

Abstract: In recent years, with the continuous increase in the scale of video data, near-duplicate video data continue to emerge, and the quality issue of video data has become more and more prominent. The video data quality can be improved through the near-duplicate videos cleaning method. However, there are few studies on near-duplicate videos cleaning, and mainly focusing on the research of near-duplicate video detection. Although the exist methods can effectively identify near-duplicatevideo data, they are difficult to automatically cleaning near-duplicate videos data and improve the quality of video data while ensuring data integrity. In order to address the above problem, it proposes a near-duplicate videos cleaning method based on deep network and FD-means clustering fusion. This method firstly uses the MOG2 model and median filter algorithm to perform background segmentation and foreground denoising. Secondly, the VGG-16 deep network model is used to extract the depth spatial features of videos. Finally, a new FD-means clustering algorithm is constructed to update the cluster center points through the generating near-duplicate video cluster siteratively, and finally near-duplicate video data are deleted outside the center points in the cluster. The experimental results show that the proposed method can effectively clean the near-duplicate videos automatically and improve data quality of the video.

Key words: video data quality, near-duplicate videos, videos cleaning, VGG-16 deep network, FD-means（feature distance-means） clustering