ARTIFICIAL INTELLIGENCE RATERS: NEURAL NETWORKS FOR RATING PICTORIAL EXPRESSION

Previous studies on classification of fine art show that features of paintings can be captured and categorized using machine learning approaches. This progress can also benefit art psychology by facilitating data collection on artworks without the need to recruit experts as raters. In this study a machine learning approach is used to predict the ratings of RizbA, a Rating instrument for two-dimensional pictorial works. Based on a pre-trained model, the algorithm was fine-tuned via transfer learning on 886 pictorial works by contemporary professional artists and non-professionals. As quality criterion, artificial intelligence raters (ART) are compared with generic raters (GR) created from the real human expert raters, using error rate and mean squared error (MSE). ART ratings have been found to have the same error range as randomly chosen human ratings. Therefore, they can be seen as equivalent to real human expert raters for almost all items in RizbA. Further training with more data will close the gap to the human raters on all items

ARTificial intelligence raters. Neural networks for rating pictorial expression

Previous studies on classification of fine art show that features of paintings can be captured and categorized using machine learning approaches. This progress can also benefit art psychology by facilitating data collection on artworks without the need to recruit experts as raters. In this study a machine learning approach is used to predict the ratings of RizbA, a Rating instrument for two-dimensional pictorial works. Based on a pre-trained model, the algorithm was fine-tuned via transfer learning on 886 pictorial works by contemporary professional artists and non-professionals. As quality criterion, artificial intelligence raters (ART) are compared with generic raters (GR) created from the real human expert raters, using error rate and mean squared error (MSE). ART ratings have been found to have the same error range as randomly chosen human ratings. Therefore, they can be seen as equivalent to real human expert raters for almost all items in RizbA. Further training with more data will close the gap to the human raters on all items

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

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.補正完畢NL