Artist-based painting classification using Markov random fields with convolution neural network

Abstract

Determining the authorship of a painting image is a challenging task because paintings of an artist may not have a unique style and various artists may have similar painting styles. In this paper, we present a new approach to categorize digital painting images based on artist. We construct a multi-scale pyramid from a painting image to consider both globally and locally the information contained in one image. For each layer, we train a Convolutional Neural Network (CNN) model to determine the class label. To build the relationship within local image patches, we employ Markov random fields (MRFs) by optimizing the Gibbs energy function defined by (1) the data term measuring the compatibility of labeling with given data, and (2) the smoothness term penalizing assignments that label neighboring patches differently. A new fusion scheme is proposed to aggregate patch-level classification results. The proposed CNN-MRF method is validated using two challenging painting image datasets. Experimental results show that the proposed method is effective and achieves state-of-the-art performance.