“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.
['Wei Ren Tan', 'Chee Seng Chan', 'Hernán E. Aguirre', 'Kiyoshi Tanaka']
Ceci n'est pas une pipe: A deep convolutional network for fine-art paintings classification