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Exploiting the Query Expansion through Knowledgebases for Images

机译:通过知识库来利用查询扩展来图像

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The evolution in the digital technology from the last few decades and different multimedia sources like Broadcast news, Movies, Videos, images, etc. have increased the size volume of the digital media daily. Due to this explosive growth in the digital media volume, it's strongly urged for the system that efficiently and effectively compiles the user demand and retrieving the relevant images. In this paper, the user query will be expanded through an open-source knowledge base WordNet and ConceptNet for retrieves the images on the base of different synonyms and concepts. This technique covers the word mismatch and word sense disambiguation (WSD) problem. Propose method effectively applied on the open benchmark image dataset LabelMe. The experimental results show that of the propose techniques have improved the retrieval performance over the traditional ones and get the accuracy up to 94% for single word and 83% for sentence word queries.
机译:从最近几十年和不同的多媒体来源等数字技术的演变,如广播新闻,电影,视频,图像等。每天增加了数字媒体的大小体积。由于这种爆炸性增长了数字媒体卷,系统强烈敦促系统有效,有效地编制用户需求和检索相关图像。在本文中,用户查询将通过开源知识库Wordnet和ConceptNet进行扩展,用于检索不同同义词和概念的基础上的图像。这种技术涵盖了不匹配和词语感歧义(WSD)问题的单词。提出方法在开放基准图像数据集标签ME上有效应用。实验结果表明,提出技术的检索性能改善了传统的检索性能,并获得了单个单词的准确性,最高可达94%,句子单词查询的83%。

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