The problem of classifying images into different predefined categories is an important high-level vision problem. In recent years, convolutional neural networks (CNNs) have been the most popular tool for image classification tasks. CNNs are multi-layered neural networks that can handle complex classification tasks if trained properly. However, training a CNN requires a huge number of labeled images that are not always available for all problem domains. A CNN pre-trained on a different image dataset may not be effective for classification across domains. In this paper, we explore the use of pre-trained CNN not as a classification tool but as a feature extraction tool for painting classification. We run an extensive array of experiments to identify the layers that work best with the problems of artist and style classification, and also discuss several novel representation and classification techniques using these features.
Banerji, S., & Sinha, A. (2016). Painting classification using a pre-trained convolutional neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10481 LNCS, pp. 168–179). Springer Verlag. https://doi.org/10.1007/978-3-319-68124-5_15