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TV Advertisement Detection For News Channels using Local Success Weighted SVM Ensemble

机译：使用本地成功加权SVM合奏的新闻频道的电视广告检测

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Research in advertisement detection (in news broadcast videos) has mainly focused on development of efficient audio-visual features. These features are used with standard machine learning algorithms to automatically segregate the advertisements. However, the discriminative abilities of features are local and may not have uniform performance through out the input space. Thus, using a fixed set of features for entire input space limits the performance of classifiers. In this paper, we use a Local Success Weighted Ensemble of SVMs (LSWE-SVM) for advertisement detection. The LSWE-SVM ensures the diversity in errors of component SVMs by training them on individual features with different similarity measures (kernels). The weight of each SVM is determined by an instance dependent "success prediction function". The success prediction functions predict high values for a particular exemplar, if the corresponding base SVMs have high likelihood of predicting the correct label of the exemplar. During training, the success prediction functions are estimated using support vector regression (SVR) trained on exemplars from cross validation sets of respective SVMs. The target for SVRs is set to 1.0 for the successfully classified exemplars and 0.0 otherwise. Given a test pattern, SVMs having high likelihood of predicting the correct label for the pattern are only allowed to contribute in the ensemble decision, thus suppressing the false decisions. Experimentations on over 150 hours of TV Broadcast news dataset have shown the superiority of LSWE-SVM over other baseline methods in terms of balanced F-score and generalization capability.
机译：广告检测（新闻广播视频中的研究）主要集中在高效的视听功能的开发。这些功能与标准机器学习算法一起使用，以自动分离广告。然而，功能的鉴别能力是局部的，并且可以通过输入空间具有均匀性能。因此，使用针对整个输入空间的固定一组特征限制了分类器的性能。在本文中，我们使用SVMS（LSWE-SVM）的本地成功加权集合进行广告检测。 LSWE-SVM通过在具有不同相似度措施（内核）的个别特征上，确保组件SVMS错误的多样性。每个SVM的权重由实例依赖的“成功预测函数”确定。如果相应的基础SVMS具有高似然预测示例的正确标签，则成功预测函数预测特定示例的高值。在训练期间，使用在各个SVM的交叉验证组上训练的支持向量回归（SVR）来估计成功预测函数。对于成功分类的示例，SVRS的目标设置为1.0，否则为0.0。鉴于测试图案，仅允许在集合决策中贡献的预测模式的正确标签的高可能性的SVM，从而抑制错误决策。超过150小时的电视广播新闻数据集的实验显示了LSWE-SVM在平衡的F分和泛化能力方面的其他基线方法。

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