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Video Caption Detection Algorithm Based on Multiple Instance Learning

机译:基于多实例学习的视频字幕检测算法

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Over the last few decades, multiple-instance learning (MIL) has been successfully utilized to solve the content-based image/video retrieval (CBIR/CBVR) problem, in which a bag corresponds to a video scene and an instance corresponds to a frame caption. However, existing feature representation schemes are not effective enough to use MIL to detect video caption frames from news video, which hinders the practical applications of CBVR. This paper presents an algorithm that regards the video frames containing caption as a bag. It detects, localizes and extracts video caption frames using multiple-instance learning (MIL) automatically. Experimental results show that the proposed method can detect, localize, and extract video caption frames with more high accuracy.
机译:在过去的几十年中,已经成功地利用了多实例学习(MIL)来解决基于内容的图像/视频检索(CBIR / CBVR)问题,其中袋对应于视频场景,并且实例对应于帧标题。然而,现有特征表示方案足够有效,以便使用MIL检测来自新闻视频的视频字幕帧,阻碍CBVR的实际应用。本文介绍了一种将包含标题为袋子的视频帧的算法。它可以自动检测,定位,本地化和提取视频字幕帧(MIL)。实验结果表明,该方法可以以更高的精度检测,本地化和提取视频字幕帧。

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