基于多示例学习的恐怖视频识别技术研究
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摘要
随着互联网的发展，与日俱增的有害信息正威胁着青少年的身心健康。其中，恐怖视频因为其媒体形式声形并茂，且没有有效的智能化手段对其进行识别和过滤，因此成为计算机视觉领域的新兴研究方向。本文主要基于多示例学习框架，针对目前恐怖视频识别技术中存在的问题展开研究。研究内容包括：(1)基于上下文关系的恐怖视频识别算法。(2)基于多视角多示例学习的恐怖视频识别算法。(3)基于判别性示例选择多示例学习的恐怖视频识别算法。三种算法分别在恐怖视频库和多示例学习基准库上进行了实验，实验结果表明，三种算法可以有效实现恐怖视频的识别，且后两种算法对其他多示例学习方法也是有效且优于传统多示例学习算法的。本文的研究对构建和谐网络环境，维护社会稳定有着重要的社会意义，并将会促进CBVR（Content-Based Video Retrieval）在视频情感理解方向的发展。
Along with the ever-growing Web comes the proliferation of objectionable content,such as pornography, violence, horror information, etc. An effective web filtering isrequired to prevent end-users, especially children, from these harmful materials.However, compared with great progress in pornography content filters, horror videosfilter is still on the stage of earlier exploration, although its threat to children’s health isno less than the former one. In this paper, three algorithms are proposed based onMIL(Multi-Instance Learning) to promote horror video scene recognition. Thealgorithms include:(1) context-aware horror video scene recognition via cost-sensitiveSparse coding;(2) horror video scene recognition based on Multi-view Multi-instanceLearning;(3) horror video scene recognition based on multi-instance learning viadiscriminative instance selection. Experiments on horror scene data and benchmark MILdatabase show that the proposed methods are effective for horror scene recognition. Inaddition,(2) and (3) are also effective for other MIL problems. The research in thispaper will help to build healthy web environment and advance the development ofCBVR in affection analysis.
Along with the ever-growing Web comes the proliferation of objectionable content,such as pornography, violence, horror information, etc. An effective web filtering isrequired to prevent end-users, especially children, from these harmful materials.However, compared with great progress in pornography content filters, horror videosfilter is still on the stage of earlier exploration, although its threat to children’s health isno less than the former one. In this paper, three algorithms are proposed based onMIL(Multi-Instance Learning) to promote horror video scene recognition. Thealgorithms include:(1) context-aware horror video scene recognition via cost-sensitiveSparse coding;(2) horror video scene recognition based on Multi-view Multi-instanceLearning;(3) horror video scene recognition based on multi-instance learning viadiscriminative instance selection. Experiments on horror scene data and benchmark MILdatabase show that the proposed methods are effective for horror scene recognition. Inaddition,(2) and (3) are also effective for other MIL problems. The research in thispaper will help to build healthy web environment and advance the development ofCBVR in affection analysis.
引文
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