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A Knowledge-Driven Multimedia Retrieval System Based on Semantics and Deep Features

机译:基于语义和深度特征的知识驱动的多媒体检索系统

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In recent years the information user needs have been changed due to the heterogeneity of web contents which increasingly involve in multimedia contents. Although modern search engines provide visual queries, it is not easy to find systems that allow searching from a particular domain of interest and that perform such search by combining text and visual queries. Different approaches have been proposed during years and in the semantic research field many authors proposed techniques based on ontologies. On the other hand, in the context of image retrieval systems techniques based on deep learning have obtained excellent results. In this paper we presented novel approaches for image semantic retrieval and a possible combination for multimedia document analysis. Several results have been presented to show the performance of our approach compared with literature baselines.
机译:近年来,由于Web内容的异质性越来越涉及多媒体内容,所需的信息已经改变了信息。虽然现代搜索引擎提供了视觉查询,但发现允许从感兴趣的特定领域搜索的系统并不容易,并通过组合文本和视觉查询来执行此类搜索。在几年和语义研究领域中提出了不同的方法许多作者提出了基于本体的技术。另一方面,在图像检索系统的背景下,基于深度学习获得了优异的结果。在本文中,我们为多媒体文档分析提出了用于图像语义检索的新方法和可能的组合。已经提出了几种结果以显示与文学基线相比的方法。

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