Abstract
As an Internet application, smart tourism has greatly enriched the tourism information. In this paper, we propose a unified modeling and expression method of attraction texts and images based on the text deep representation model and convolution neural network. According to the cross-media characteristics of tourism big data, we propose a semantic learning and analysis method for cross-media data, and correlate tourism texts with images based on deep features and topic semantics. Experimental results show that the proposed method can achieve better results for semantic analysis and cross-media retrieval of tourism big data.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (No. 61320106006, No. 61532006, No. 61502042).
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Li, Y., Du, J., Lin, Z., Ye, L. (2017). Cross-Media Retrieval of Tourism Big Data Based on Deep Features and Topic Semantics. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_11
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DOI: https://doi.org/10.1007/978-3-319-68935-7_11
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