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A systemic approach to automatic metadata extraction from multimedia content

机译:从多媒体内容自动提取元数据的系统方法

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摘要

There is a need for automatic processing and extracting of meaningful metadata from multimedia information,udespecially in the audiovisual industry. This higher level information is used in a variety of practices, such as enrichingudmultimedia content with external links, clickable objects and useful related information in general. This paper presents a system for efficient multimedia content analysis and automatic annotation within a multimedia processing and publishing framework. This system is comprised of three modules: the first provides detection of faces and recognition of known persons; the second provides generic object detection, based on a deep convolutional neural network topology; the third provides automated location estimation and landmark recognition based on state-of-the-art technologies. The results are exported in meaningful metadata that can be utilized in various ways. The system has been successfully tested in the framework of the EC Horizon 2020 Mecanex project, targeting advertising and production markets.
机译:特别是在视听行业中,需要自动处理和从多媒体信息中提取有意义的元数据。更高级别的信息用于各种实践中,例如通过外部链接,可点击对象和有用的相关信息来丰富 ud多媒体内容。本文提出了一种在多媒体处理和发布框架内进行高效多媒体内容分析和自动注释的系统。该系统由三个模块组成:第一个模块提供面部检测和已知人员识别;第二种是基于深度卷积神经网络拓扑的通用对象检测。第三部分基于最新技术提供自动位置估计和地标识别。结果以有意义的元数据导出,该元数据可以多种方式使用。该系统已经在针对广告和生产市场的EC Horizo​​n 2020 Mecanex项目框架中成功测试。

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