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An Inductive Logic Programming-Based Approach for TV Stream Segment Classification

机译:基于归纳逻辑编程的电视流片段分类方法

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This paper proposes a method for classifying TV stream segments as long programs or inter-programs (IP). As almost all IPs are broadcasted several times in the TV stream, a first segmentation step based on a repeated sequence detection is performed. Resulting segments (the occurrences of repeated sequences and the rest of the stream) have to be classified. The proposed solution for that is based on Inductive Logic Programming. In addition to intrinsic features of each segment (e.g.duration), our technique makes use of the relational and contextual information of the segments in the stream. The effectiveness of our solution has been shown on a real TV stream of 6~days and a comparative study with an SVM-based classification approach has been performed.
机译:本文提出了一种将电视流片段分类为长节目或节目间(IP)的方法。由于几乎所有IP在TV流中广播了几次,因此执行了基于重复序列检测的第一个分段步骤。必须对结果段(重复序列的出现和流的其余部分)进行分类。为此提出的解决方案基于归纳逻辑编程。除了每个分段的固有特征(例如持续时间)外,我们的技术还利用了流中各个分段的关系和上下文信息。我们的解决方案的有效性已在6天的真实电视流中显示,并已进行了基于SVM的分类方法的比较研究。

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