The classification of oil paintings is important for the generation,recognition and application of oil paintings.However,due to the large difference in texture between oil paintings and ordinary pictures,and the personalized creation of oil painters,and the higher uncertainty,it becomes more difficult to classify oil paintings.Taking the classification of oil paintings containing bridges as an example,this paper proposed a method for oil painting classification based on the nearest-neighbor convolutional neural network.Utilizing the K-nearest neighbor(KNN)algorithm to extract the K closest training samples,we investigated the deep features of the oil paintings with the convolutional neural network and distinguished the objects in them.We discussed in detail the data processing,architecture design and training process of convolutional neural networks.Through the analysis and comparison of this method on kaggle dataset,we selected three datasets for experiments.The results show that this method has improved the accuracy by an average of 2.4％compared to the nearest neighbor algorithm,3.1％compared to the convolutional neural network,and 6.9％compared to the support vector machine method.
维普
万方数据