Investigation of Visual Language Landscape of Tourist Attractions from Multimodal Perspective

With the development of economic globalization, the tourism industry has been welcomed by the public. The visual language landscape of tourist attractions can not only assist tourists to play and watch the project, but if it is properly planned, the language landscape can also become a major feature and highlight of the scenic spot. Therefore, how to set up and construct the visual language landscape of tourist attractions is a problem that needs to be considered in each region. In response to the above problems, on the basis of understanding the concept types of the visual language landscape of tourist attractions, this article conducts in-depth research and investigation on the visual language landscape of tourist attractions, combining the evaluation dataset in the multimodal perspective and the Convolutional Neural Network (CNN) –Recurrent Neural Network (RNN) model based on semantic regularization. This article conducted a comparative experiment on each model on the NUS-WIDE dataset and the MS-COCO dataset. The experimental results showed that it was crucial to give full play to the expressive power of the CNN. Compared to the NUS-WIDE dataset, the MS-COCO dataset brought less additional boost by leveraging social media tags. The CIDEr score of the CNN-RNN model based on semantic regularization was improved by 11.4%, which placed the foundation for the investigation and analysis of the linguistic landscape of tourist attractions.