Exploring the visual distinguishability and topic autocorrelation of murals unearthed in China from the spatial perspective

Abstract

Murals unearthed in China have outstanding regional characteristics and one of the largest period spans in scale and variety. To explore the visual distinguishability and topic autocorrelation of murals unearthed in China from the spatial perspective, multiple classification models are employed to classify murals unearthed in China through visual features. Then, the k-means is employed to mine topics, and they are analysed through topic intensities (TIs), Moran’s Index (MI) and spatial topic concentration degrees (STCDs). In addition, the characteristics of topic distribution and evolution are summarised and revealed in the spatial dimension. From a spatial perspective, it verifies the distinguishability of visual features of murals through ViT_BOW_GNB, and the precision of this model is 98.17%. Thirteen topics are clustered through k-means, and the distribution of mural topics is spatial autocorrelation according to MI. Besides, the topic evolves from the political centre to the surrounding area, and the topics with high intensities are highly concentrated in spatial. This study reveals the spatial characteristics of the mural at the level of visual features and semantics, which facilitates the digital management, conservation and knowledge discovery of cultural heritage resources.