Game-powered machine learning
Game-powered machine learning
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DOI：
10.1073/pnas.1014748109
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发表时间：
2012-04-24
影响因子：
11.1
通讯作者：
Lanckriet, Gert
中科院分区：
文献类型：
--
作者：
Barrington, Luke;Turnbull, Douglas;Lanckriet, Gert
通过用大量相关的语义关键字或标签精确地注释每个图像、视频或歌曲来促进在大量多媒体信息中搜索相关内容。我们介绍了游戏驱动的机器学习，这是一种注释多媒体内容的集成方法，通过在线游戏将人类计算的有效性与机器学习的可扩展性相结合。我们研究这个框架标记音乐。首先，一个面向社会的音乐注释游戏称为羊群它收集可靠的音乐注释的基础上，“智慧的人群。其次，这些注释的示例用于训练监督机器学习系统。第三，机器学习系统主动引导注释游戏收集最有利于未来模型迭代的新数据。一旦经过训练，该系统就可以自动注释一个比单独使用人类计算所能标记的大得多的音乐语料库。自动注释的歌曲可以基于它们与基于文本的查询的语义相关性来检索（例如，“带有萨克斯管的时髦爵士乐”、“幽灵般的电子乐”等）。基于本文提出的结果，我们发现，积极耦合注释游戏与机器学习提供了一个可靠的和可扩展的方法，使可搜索的大量多媒体数据。
Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data.