The application of Hyperpixel segmentation algorithm in Chinese painting

Abstract

Chinese painting, as an important component of traditional Chinese art, not only expresses Chinese culture and history, but also contains rich artistic connotations. Therefore, extracting and analyzing the main objects of Chinese painting is of great significance. The research introduces a fusion deep network based super pixel segmentation algorithm for image segmentation, and utilizes user marked segmentation target objects and backgrounds. The maximum similarity region merging idea is adopted to extract the main subject objects in Chinese painting, thereby achieving a deep understanding of the work. The results show that the boundary recall and segmentation accuracy of the Chinese painting subject object segmentation algorithm based on super pixels on the BuptPainting dataset are as high as 94.38% and 98.06%, respectively, with an undersegmentation error rate of only 7.69%. Meanwhile, the average accuracy value, optimal data scale value, and optimal image scale value of this method on the BuptPainting dataset are as high as 0.657, 0.541, and 0.529, respectively. The method proposed by the research institute has significant advantages in segmenting the main objects of Chinese painting, providing new ideas and methods for image analysis and processing of modern Chinese painting, and helping to further explore the artistic charm and cultural connotations of Chinese painting.