Abstract
Data detailed on Support Vector Machines have been presented. According to news reporting out of Wuhan, People’s Republic of China, by NewsRx editors, research stated, “Dunhuang is a unique art treasure and a world heritage site. In order to organise and manage Dunhuang cultural heritage resources, this article studies the classification of Dunhuang murals in different dynasties, and explores the topic distribution characteristics and evolution rules of them.” Funders for this research include National Natural Science Foundation of China (NSFC), Key Research Institutes of Philosophy and Social Science by Ministry of Education, PR China. Our news journalists obtained a quote from the research from Wuhan University, “First, image features are extracted through scale-invariant feature transform (SIFT) and Canny and scale-invariant feature transform (CSIFT), a visual dictionary is generated through the k-means clustering algorithm, and the term frequency-inverse document frequency (TF-IDF) vector is calculated and combined with the colour feature vector extracted via hue, saturation and value (HSV). Second, Dunhuang mural images are collected and the support vector machine (SVM) classifier is built. Finally, the knowledge graph-based topic maps are constructed, and graph theory is introduced to analyse the topic distribution and evolution of Dunhuang murals in different dynasties. The results show that the Dunhuang murals of different dynasties can be effectively classified through the bag of words, HSV and support vector machine (BOW_HSV_SVM) based on their visual features.”
NETL
NSTL
Sage