Ceci n'est pas une pipe: A deep convolutional network for fine art paintings classification
"Ceci n'est pas une pipe" French for "This is not a pipe" This is the description painted on the first painting in the figure above. But to most of us, how could this painting is not a pipe, at least not to the great Belgian surrealist artist Rene Magritte. He said that the painting is not a pipe, but rather an image of a pipe. In this paper, we present a study on large scale classification of fine art paintings using the Deep Convolutional Network. Our objectives are two folds. On one hand, we would like to train an end to end deep convolution model to investigate the capability of the deep model in fine art painting classification problem. On the other hand, we argue that classification of fine art collections is a more challenging problem in comparison to objects or face recognition. This is because some of the artworks are non representational nor figurative, and might requires imagination to recognize them. Hence, a question arose is that does a machine have or able to capture "imagination" in paintings? One way to find out is train a deep model and then visualize the low level to high level features learnt. In the experiment, we employed the recently publicly available large scale "Wikiart paintings" dataset that consists of more than 80,000 paintings and our solution achieved state of the art results (68% in overall performance.
2016 IEEE International Conference on Image Processing (ICIP)