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Domain-Oriented Subject Aware Model for Multimedia Data Retrieval

机译:面向领域的多媒体数据检索主题感知模型

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With the increment of the scale of internet information as well as the cross-correlation interaction, how to achieve accurate retrieval of multimedia data is an urgent question in terms of efficiently utilizing information resources. However, existing information retrieval approaches provide only limited capabilities to search multimedia data. In order to improve the ability of information retrieval, we propose a domain-oriented subject aware model by introducing three innovative improvements. Firstly, we propose the text-image feature mapping method based on the transfer learning to extract image semantics. Then we put forward the annotation document method to accomplish simultaneous retrieval of multimedia data. Lastly, we present subject aware graph to quantify the semantics of query requirements, which can customize query threshold to retrieve multimedia data. Conducted experiments show that our model obtained encouraging performance results.
机译:随着因特网信息规模的增加以及互相关交互,从有效利用信息资源的角度来看,如何实现多媒体数据的准确检索已成为当务之急。但是,现有的信息检索方法仅提供有限的能力来搜索多媒体数据。为了提高信息检索的能力,我们通过引入三项创新改进提出了一种面向领域的主题感知模型。首先,提出一种基于转移学习的文本图像特征映射方法,以提取图像语义。然后提出了注释文档的方法来完成多媒体数据的同时检索。最后,我们提出了主题感知图来量化查询需求的语义,它可以自定义查询阈值以检索多媒体数据。进行的实验表明,我们的模型获得了令人鼓舞的性能结果。

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