Detection and Identification of Rare Audiovisual Cues
Detection and Identification of Rare Audiovisual Cues
复制标题
罕见视听线索的检测和识别
DOI：
10.1007/978-3-642-24034-8_9
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Almajai I
中科院分区：
文献类型：
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作者：
Almajai I
机器感知的一个关键问题是如何自适应地建立在现有能力之上，从而允许新的功能。这其中隐含着异常检测和学习迁移的概念。一个感知系统必须首先确定现有的学习模型在什么时候停止应用，其次，现有模型的哪些方面可以应用于新定义的学习领域。因此，异常必须与异常值区分开来，即学习模型未能产生明确响应的情况；在现有模型中区分新的（但有意义的）输入和错误分类错误也是必要的。因此，我们应用了一种基于比较强弱分类器[10]输出的异常检测方法来检测从单打到双打网球视频转换中涉及的规则不一致问题。然后，我们演示了如何使用检测到的异常将学习从一个（最初已知的）规则治理结构转移到另一个。我们的最终目标是在现有解说技术的基础上，构建一个基于球场的体育视频解说自适应系统。
A key question in machine perception is how to adaptively build upon existing capabilities so as to permit novel functionalities. Implicit in this are the notions ofanomaly detectionandlearning transfer. A perceptual system must firstly determine at what point the existing learned model ceases to apply, and secondly, what aspects of the existing model can be brought to bear on the newly-defined learning domain.Anomaliesmust thus be distinguished from mereoutliers, i.e. cases in which the learned model has failed to produce a clear response; it is also necessary to distinguish novel (but meaningful) input from misclassification error within the existing models. We thus apply a methodology of anomaly detection based on comparing the outputs of strong and weak classifiers [10] to the problem of detecting the rule-incongruence involved in the transition from singles to doubles tennis videos. We then demonstrate how the detected anomalies can be used to transfer learning from one (initially known) rule-governed structure to another. Our ultimate aim, building on existing annotation technology, is to construct an adaptive system for court-based sport video annotation.