面向上下文的Web视频标签推荐
Context-Oriented Web Video Tag Recommendation
摘要：
标签推荐是丰富多媒体内容文本注释的常用方法。然而，现有技术的推荐方法是建立在配对标签相关性上的，其几乎不能捕获web视频的上下文，即，谁在哪里做什么。在本文中，我们提出了面向上下文的标签推荐（CtextR）的方法，它扩展了标签的上下文一致的约束下的Web视频。给定一个Web视频，CtextR首先收集与视频描述相同事件的多种形式的WWW资源，这些资源产生信息丰富且一致的上下文，然后基于所获得的上下文进行标签推荐。在80,031个网络视频集合上的实验表明，CtextR为网络视频推荐了各种相关标签。此外，丰富的标签提高了Web视频分类的性能。
英文摘要：
Tag recommendation is a common way to enrich the textual annotation of multimedia contents. However, state-of-the-art recommendation methods are built upon the pair-wised tag relevance, which hardly capture the context of the web video, i.e., when who are doing what at where. In this paper we propose the context-oriented tag recommendation (CtextR) approach, which expands tags for web videos under the context-consistent constraint. Given a web video, CtextR first collects the multi-form WWW resources describing the same event with the video, which produce an informative and consistent context; and then, the tag recommendation is conducted based on the obtained context. Experiments on an 80,031 web video collection show CtextR recommends various relevant tags to web videos. Moreover, the enriched tags improve the performance of web video categorization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
