Classification of Oil Painting Art Style Based on Multi-feature Fusion

• Published in: Ji suan ji ke xue Vol. 50; no. 3; pp. 223 - 230
• Main Authors: Xie, Qinqin, He, Lang, Xu, Ruli
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.03.2023; Editorial office of Computer Science
• Subjects: Algorithms; art style of oil painting|image classification|spatial feature|feature fusion|spatial voting; Classification; Classifiers; Feature extraction; Model accuracy; Oil painting
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.211200110

The existing oil painting art style classification algorithms ignore the influence of the main area and the overall effect on the art style.Aiming at this problem, this paper proposes a new oil painting classification algorithm based on multi-feature fusion classifier（MFFC）.Firstly, based on the common arrangement form of oil painting art elements, this paper designs the overlapping image block method.This method extracts spatial features of oil paintings to make up for the lack of composition style in existing algorithms.And it also can be used to distinguish the subject area from the background area.Secondly, the spatial features and the underlying features are combined in series to increase the location information of the elements in the picture.Finally, the spatial voting method is designed to give priority to the classification result of the main area as the output result of the algorithm.This is to highlight the role of oil painting subject area in the classification and realize the automatic classifica