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A Compatible Model of Unstructured Data for Cross-Media Retrieval in the Field of Tourism

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Intelligent Data Engineering and Automated Learning – IDEAL 2016 (IDEAL 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9937))

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Abstract

With the development of multimedia, the amount of unstructured data of multimedia is increasing, especially for the tourism. Meanwhile, it is one of the key issue to analysis large-scale unstructured data, which helps us to find the hidden relevance between redundant and different data. How to retrieve efficiently, and recommend accurately for cross-media retrieval is more and more important. This paper proposes a new data mode for cross-media retrieval - unstructured data compatible model, short for UDC model. The UDC model is constructed by its own metadata. All metadata are organized by a certain hierarchical relationship. Every metadata consists of three layers: the feature layer, the semantic layer and the compatibility layer. Furthermore, this paper presents retrieval and recommendation algorithms based on UDC model. The experiment results demonstrate that the retrieval engine based on UDC mode can be more effective for cross-media retrieval and recommendation.

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Acknowledgement

This work is supported by the National Science Foundation of China (Grant Nos. 61502082), the Fundamental Research Funds for the Central Universities, ZYGX2014J065 and the Smart Cities Foundation of Sichuan (Grant Nos. RWS-CYHKF-04-2015003).

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Authors and Affiliations

  1. Chengdu Research Institute of UESTC, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, People’s Republic of China

    Han Hu

  2. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, People’s Republic of China

    Han Hu, Xiaoyu Li, Wenyi Wu & Zhaoyi Liu

Authors
  1. Han Hu
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  2. Xiaoyu Li
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  3. Wenyi Wu
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  4. Zhaoyi Liu
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Corresponding authors

Correspondence to Han Hu or Xiaoyu Li .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. Nanjing University, Nanjing, China

    Yang Gao

  3. Yangzhou University, Yangzhou, Jiangsu, China

    Bin Li

  4. Aeronautics and Astronautics, Nanjing University Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. Nanjing Normal University, Nanjing, China

    Ming Yang

  6. Yangzhou University, Yangzhou, Jiangsu, China

    Yun Li

  7. Ostfalia University of Applied Sciences, Wolfenbüttel, Germany

    Frank Klawonn

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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© 2016 Springer International Publishing AG

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Hu, H., Li, X., Wu, W., Liu, Z. (2016). A Compatible Model of Unstructured Data for Cross-Media Retrieval in the Field of Tourism. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-46257-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46256-1

  • Online ISBN: 978-3-319-46257-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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