Artwork Recognition and Classification Algorithm Based on Computer Vision

This paper centers on the research of the artwork recognition and classification algorithm based on computer vision. During the research process, first, a vast image dataset of artworks encompassing a wide variety of artistic styles, genres, and periods was amassed. The convolutional neural network (CNN) in deep learning was adopted as the base model, and the pre-trained model was fine-tuned through transfer learning techniques to adapt to the feature extraction of artworks. The cross-validation approach was ingeniously deployed to fine-tune the model parameters and boost the algorithm's generalization prowess. The experimental outcomes have clearly ascertained that this algorithm has attained a rather elevated echelon in the accuracy of artwork recognition. It is eminently capable of discerning artworks of diverse styles and genres with dexterity, be it Impressionism, Realism, and the like. In terms of classification performance, it can also achieve relatively accurate classification for complex artworks, providing reliable technical support for fields such as the digital management of artworks and art history research.