Research on Automatic Classification and Recognition System of Works of Art Based on Computer Vision

In this study, an automatic classification and recognition system based on computer vision is constructed based on ancient ceramic art works. The system uses advanced visual feature extraction technology, which can accurately extract the unique visual features of ancient ceramic works from images, including but not limited to enamel color, decoration, type, etc. At the heart of the research lies the development of innovative feature extraction algorithms that combine deep learning and traditional image processing techniques to effectively separate the subject of an artwork from a complex background and accurately identify subtle visual differences. Through training and testing on many ancient ceramic image data, the algorithm shows excellent classification performance and recognition accuracy. In the system simulation stage, a series of standardized test sets are used to evaluate the classification and recognition ability of the system. The simulation results show that the system can maintain a high level of recognition accuracy even in the face of diverse and complicated ancient ceramic works, which proves its reliability and effectiveness in practical applications.