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首页> 外文会议>IEEE International Conference on Multimedia and Expo Workshops >Kuukkeli-TV: Online content-based services and applications for broadcast TV with long-term user experiments
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Kuukkeli-TV: Online content-based services and applications for broadcast TV with long-term user experiments

机译：Kuukkeli-TV：具有长期用户实验的基于在线内容的广播电视服务和应用

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摘要

Online TV services have facilitated time-shifted TV viewing. Accordingly, new service concepts are needed to improve access to relevant information in the broadcast TV content. In this paper we introduce a system that indexes TV broadcast in near real-time from seven free-to-air television channels. Our system uses machine learning and data mining techniques to extract descriptive novelty word summaries and picture highlights automatically from TV program subtitles and uses them to provide non-linear content-based access to relevant TV content fragments in novel end-user services and applications. First end-user service allows browsing of recent time shifted TV content using time and program genre metaphors with extracted program summaries. Second end-user service provides free-text search of archival and time shifted TV content. Additionally, the system allows content-based recommendation of similar TV content from the database of 180 000 indexed programs. Recommendations are used in the enduser services and Mobile EPG Guide application. Over 5 000 user sessions have been collected to study how users adopt our content-based access metaphors to examine interesting TV content. User logs revealed that the proposed contentbased access techniques were more popular in TV program search and browsing activities than conventional techniques based on program title and description metadata.
机译：在线电视服务促进了时移电视的观看。因此，需要新的服务概念来改善对广播电视内容中的相关信息的访问。在本文中，我们介绍了一种系统，该系统可对来自七个免费电视频道的电视广播进行实时索引。我们的系统使用机器学习和数据挖掘技术从电视节目字幕中自动提取描述性新颖的单词摘要和图片亮点，并使用它们在新颖的最终用户服务和应用程序中提供基于非线性内容的对相关电视内容片段的访问。第一最终用户服务允许使用时间和节目类型隐喻以及提取的节目摘要来浏览最近时移的电视内容。第二个最终用户服务提供了存档和时移电视内容的自由文本搜索。另外，该系统允许从18万个索引节目的数据库中基于内容的类似电视内容推荐。建议在最终用户服务和“移动EPG指南”应用程序中使用。已经收集了5000多个用户会话，以研究用户如何采用我们基于内容的访问隐喻来检查有趣的电视内容。用户日志显示，与基于节目标题和描述元数据的常规技术相比，基于内容的访问技术在电视节目搜索和浏览活动中更为流行。

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