Investigation of Visual Language Landscape of Tourist Attractions from Multimodal Perspective

Abstract

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 paper conducts in-depth research and investigation on the visual language landscape of tourist attractions, combining the evaluation data set in the multi-modal perspective and the CNN RNN model based on semantic regularization. This paper 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 convolutional neural network (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-Recurrent Neural Network (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. Visibility Language Landscape, Multimodal View, Tourist Attractions, Semantic Regularization, Convolutional Neural Network