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Tourism scene classification based on multi-stage transfer learning model

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Abstract

In the past, many researchers focus on scene classification in computer vision, because it is an important problem. Tourism scene classification, however, has not been paid attention to in the field of computer vision. In this paper, we introduce a new scenic-spots-centric database called tourism scene, which consists of 25 tourism scenic areas with 750 tourism scene categories, about 440 thousand labeled images. For tourism scene classification, we propose a multi-stage transfer learning model with category hierarchical structure and use convolutional neural networks (e.g., AlexNet) as basic building block. To demonstrate the effectiveness of our proposed model, we also propose a baseline model and one-stage transfer learning model. From the results, we observe that our proposed framework achieves new bounds for performance.

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Acknowledgements

This research is partly supported by the National Nature Science Foundation of China (U1611461, 61672241 and 61528204), the Cultivation Project of Major Basic Research of NSF-Guangdong Province (2016A030308013), Guangdong Key Research Base of Technology and Finance (2014B030303005) and Guangdong Provincial Key Laboratory of Technology and Finance & Big Data Analysis (2017B030301010). Yong Xu is also a visiting researcher with Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen 518060, China.

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Tangquan Qi, Yong Xu & Haibin Ling

  2. Information Science Department, Center for Information Science and Technology Computer, Temple University, Philadelphia, PA, USA

    Haibin Ling

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  1. Tangquan Qi
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  2. Yong Xu
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  3. Haibin Ling
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Correspondence to Yong Xu.

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Tangquan Qi, Yong Xu and Haibin Ling state that there are no conflicts of interest.

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Qi, T., Xu, Y. & Ling, H. Tourism scene classification based on multi-stage transfer learning model. Neural Comput & Applic 31, 4341–4352 (2019). https://doi.org/10.1007/s00521-018-3351-2

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