Tag-based Video Retrieval with Social Tag Relevance Learning
Tag-based Video Retrieval with Social Tag Relevance Learning
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DOI：
10.1109/gcce46687.2019.9015338
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Hiroshi Takeda;Soh Yoshida;M. Muneyasu
中科院分区：
文献类型：
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作者：
Hiroshi Takeda;Soh Yoshida;M. Muneyasu
高质量的标签在多媒体信息检索等应用中起着重要的作用。提出了一种基于数据驱动的社会标签相关性学习方法，以提高基于标签的视频检索性能。标签相关性是指标签与多媒体内容的相关程度。为了学习标签的相关性，我们应用了一个著名的标签邻居投票算法，它积累了来自视觉邻居的投票。然而，数据集之间标签数量的不平衡导致标签投票的准确性损失。因此，在所提出的方法中，我们研究了一个公式，用于计算标签的相关性分数考虑标签出现频率的不平衡。在YouTube-8 M数据集上进行了实验，结果表明该方法是有效的。
High-quality tags play an important role in many applications such as multimedia information retrieval. This paper proposes a social tag relevance learning method using a data-driven approach to improving tag-based video retrieval performance. The tag relevance means how a tag is relevant to multimedia content. To learn the tag relevance, we apply a well-known tag neighbor voting algorithm, which accumulates votes from visual neighbors. However, an imbalance in the number of tags among the datasets causes a loss in the accuracy of tag voting. Therefore, in the proposed method, we examine a formula for calculating the tag relevance score considering the tag occurrence frequency imbalance. We conduct experiments on the YouTube-8M dataset, and the results show that our approach is effective and efficient.