Deep Learning-based Recognition and Quantification of Cultural Identity Features in Chinese Oil Painting Art

Chinese oil painting art is an important carrier of contemporary Chinese cultural identity features, the identification and quantitative study of the color and texture of the picture can help to understand the characteristics of the oil painting works more deeply.Therefore, this paper proposes a feature recognition method for oil painting art based on deep learning method.The Otsu threshold method and DeeplabV3+ network model based on DeeplabV3+ are selected for image graying and segmentation processing.The global color histogram and ring LBP are used to extract the color and texture features of the picture respectively, and the oil painting feature recognition is completed based on the regularized limit learning machine.In several sets of quantitative results, the methods in this paper all have better oil painting color and texture feature recognition, among which the RELM algorithm has the highest detection accuracy at low correlation features.It shows that the deep learning based Chinese oil painting art and cultural identity feature recognition method can effectively extract oil painting features and realize the quantitative research on oil painting.