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Measuring the Semantic Relatedness Between Images Using Social Tags

Author

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  • Zheng Xu

    (The Third Research Institute of Ministry of Public Security, Shanghai, China)

  • Zhiguo Yan

    (The Third Research Institute of Ministry of Public Security, Shanghai, China)

  • Yunhuai Liu

    (The Third Research Institute of Ministry of Public Security, Shanghai, China)

  • Lin Mei

    (The Third Research Institute of Ministry of Public Security, Shanghai, China)

Abstract

Relatedness measurement between multimedia such as images and videos plays an important role in computer vision, which is a base for many multimedia related applications including clustering, searching, recommendation, and annotation. Recently, with the explosion of social media, users can upload media data and annotate content with descriptive tags. In this paper, the authors aim at measuring the semantic relatedness of Flickr images. Firstly, information theory based functions are used to measure the semantic relatedness of tags. Secondly, the integration of tags pair based on bipartite graph is proposed to remove the noise and redundancy. The data sets including 1000 images from Flickr are used to evaluate the proposed method. Two data mining tasks including clustering and searching are performed by the proposed method, which shows the effectiveness and robust of the proposed method.

Suggested Citation

  • Zheng Xu & Zhiguo Yan & Yunhuai Liu & Lin Mei, 2013. "Measuring the Semantic Relatedness Between Images Using Social Tags," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 7(2), pages 1-12, April.
    • Handle: RePEc:igg:jcini0:v:7:y:2013:i:2:p:1-12
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