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Smart trailer: Automatic generation of movie trailer using only subtitles

机译:智能预告片:仅使用字幕自动生成电影预告片

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With the enormous growth rate in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently. Video summarization is considered a promising approach for efficacious realization of video content through Identifying and picking out descriptive frames of the video. In this paper, we propose an adaptive framework called Smart-Trailer (S-Trailer) to automate the process of creating an online trailer for any movie based only on its subtitle. The language used in the subtitle is English. The framework analyzes the movie subtitle file to extract relevant textual features that are used to classify the movie into its corresponding genre(s). Initial experimentation resulted in generating genre-classification corpus. The generated corpus is tested against real movies dataset and showed high classification accuracy rate (0.89) in classifying movies into their corresponding genre(s). The proposed system returned automated trailers that contain on average 47% accuracy in terms of recalling scenes appeared on the original movie trailer for different movie genres. Currently, we employ deep learning techniques to captures user behaviors and opinions in order to adapt our system to provide users with relevant video scenes recommendations that match their preferences.
机译:随着用户生成视频的巨大增长,能够有效地进行导航变得越来越重要。视频摘要被认为是通过识别和挑选视频的描述性帧有效实现视频内容的一种有前途的方法。在本文中,我们提出了一个名为Smart-Trailer(S-Trailer)的自适应框架,以自动为仅根据字幕创建任何电影的在线预告片。字幕中使用的语言是英语。框架分析电影字幕文件,以提取相关的文本特征,这些特征用于将电影分类为相应的流派。最初的实验导致生成体裁分类语料库。针对真实电影数据集测试了生成的语料库,并在将电影分类为相应流派时显示出较高的分类准确率(0.89)。所提出的系统返回的自动预告片,其回忆场景出现在原始电影预告片上的不同电影流派的平均准确率达到47%。当前,我们采用深度学习技术来捕获用户的行为和意见,以使我们的系统适应于向用户提供与其偏好相匹配的相关视频场景建议。

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