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首页> 外文期刊>International journal of information and decision sciences >Association rule mining with a correlation-based interestingness measure for video semantic concept detection
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Association rule mining with a correlation-based interestingness measure for video semantic concept detection

机译：关联规则挖掘与基于相关度的兴趣度度量用于视频语义概念检测

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摘要

Association rule mining (ARM) has been adopted in automatic semantic concept detection to discover the association patterns from the multimedia data and predict the target concept classes. As a rule-based method, ARM faces the challenges on rule pruning. Such challenges could be addressed by utilising proper interestingness measures. In this paper, a video semantic concept detection framework that uses ARM together with a novel correlation-based interestingness measure is proposed. The interestingness measure is obtained from applying multiple correspondence analysis (MCA) to capture the correlation between features and concept classes. This new correlation-based interestingness measure is first used in the rule generation stage, and then reused and combined with the inter-similarity and intra-similarity values to select the final rule set for classification. Experimented with 14 concepts from the benchmark TRECV1D data, our proposed framework achieves higher accuracy than the other six classifiers that are commonly used in semantic concept detection.
机译：自动语义概念检测已采用关联规则挖掘（ARM），以从多媒体数据中发现关联模式并预测目标概念类别。作为基于规则的方法，ARM面临规则修剪方面的挑战。这些挑战可以通过采取适当的有趣措施来解决。本文提出了一种视频语义概念检测框架，该框架将ARM与基于相关性的新颖性度量结合使用。通过应用多重对应分析（MCA）来捕获要素与概念类之间的相关性，可以得出有趣程度。这种新的基于相关性的兴趣度度量首先在规则生成阶段中使用，然后重新使用并与相似度和相似度内值组合以选择最终的分类规则集。与基准TRECV1D数据一起对14个概念进行了实验，我们提出的框架比语义概念检测中常用的其他六个分类器具有更高的准确性。

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