Application of cultural elements of dunhuang murals in landscape design based on mean shift algorithm extraction

Abstract

Although picture extraction is challenging, the murals at Dunhuang are historically significant and offer rich content. The work suggests an image segmentation model based on the Mean Shift algorithm and an area salience prioritisation model to extract the cultural aspects in the Dunhuang murals for landscape design. First, an image segmentation model based on the Mean Shift algorithm is established, and then a region salience value calculation method and a region prioritisation method are designed to establish a region salience prioritisation model. The outcomes showed that a segmentation model built using the Mean Shift algorithm in the study processed a 405175 image with a processing time of 3.18 seconds, an edge integrity rate of 88.9%, an accuracy rate of 87.4%, an F-value of 88.7%, and a total of 302 regions. The segmented Dunhuang image featured few noise points and a distinct shape. Salient region transfer path is more regular and more in line with the human visual transfer mechanism thanks to the research design of the region saliency value calculation method, which also improves saliency detection performance. The highest correct rate when dividing the image is 0.97, the highest check rate is 0.8, and the highest F1 value is 1. In conclusion, the study’s methodology has some favourable implications for landscape design and may be effectively used to extract cultural components from photographs.