For analyzing digital images of paintings we propose a new approach to categorize them based on artist. Determining the authorship of a painting is challenging because common subjects are illustrated in paintings, and paintings of an artist may not have a unique style. The proposed approach is built upon convolutional neural networks (CNN) a class of biologically inspired vision model that recently demonstrates near-human performance on several visual recognition tasks. However, training a CNN model requires large scale training data of a fixed input image size (e.g. 227 x 227). In this thesis, we propose to construct a multi-layer pyramid from an image, providing 21X more features than using a single layer (i.e., the original image) alone. We train a CNN model for each layer, and consider relationship within neighborhood patches in layers that have fine sub-regions, and propose a new weighted fusion scheme to aggregate the three layers adaptively. To evaluate the proposed methods, we collect two painting image datasets. The first dataset is categorized into 13 artists while the second one is categorized into 23 artists. As demonstrated in the experimental results, the proposed methods surpass the baseline method significantly in terms of precision, recall, and F-score to prove the effectiveness of proposed methods.

For analyzing digital images of paintings we propose a new approach to categorize them based on artist. Determining the authorship of a painting is challenging because common subjects are illustrated in paintings, and paintings of an artist may not have a unique style. The proposed approach is built upon convolutional neural networks (CNN) a class of biologically inspired vision model that recently demonstrates near-human performance on several visual recognition tasks. However, training a CNN model requires large scale training data of a fixed input image size (e.g. 227 x 227). In this thesis, we propose to construct a multi-layer pyramid from an image, providing 21X more features than using a single layer (i.e., the original image) alone. We train a CNN model for each layer, and consider relationship within neighborhood patches in layers that have fine sub-regions, and propose a new weighted fusion scheme to aggregate the three layers adaptively. To evaluate the proposed methods, we collect two painting image datasets. The first dataset is categorized into 13 artists while the second one is categorized into 23 artists. As demonstrated in the experimental results, the proposed methods surpass the baseline method significantly in terms of precision, recall, and F-score to prove the effectiveness of proposed methods.

[1] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrel, "Caffe: convolutional architecture for fast feature embedding," ACM International Conference in Multimedia, pp. 675-678, 2014.
[2] Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel, "Backpropagation applied to hand-written zip code recognition,"Neural Computing, vol. 1, no. 4, pp. 541-551, 1989.
[3] A. Krizhevsky, I. Sutskever, and G. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems 25, pp. 1097-1105, 2012.
[4] B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, "Learning deep features for scene recognition using places database," Advances in Neural Information Processing System, 2014.
[5] S. Karayev, "Recognizing image style," British Machine Vision Conference, 2014.
[6] J. Long, E. Shelhamer, and T. Darrel, "Fully convolutional networks for semantic segmentation," Computer Vision and Pattern Recognition, pp. 3431-3440, 2015.
[7] J. Donahue, "Long-term recurrent convolutional networks for visual recognition and description," Computer Vision and Pattern Recognition, pp. 2625-2634, 2015.
[8] M. Kummerer, L. Theis, and M. Bethge, "Deep gaze i: Boosting saliency prediction with feature maps trained on imagenet," ICLR Workshop, 2015.
[9] PaintingDb, "PaintingDb fastest growing art gallery in the web," 2015.
[10] WikiArt, "WikiArt the online home for visual arts from all around the world.," 2016.
[11] Q. Hanchao and H. Shannon, "A new method for visual stylometry on impresionist paintings," IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2036-2039, 2011.
[12] M. Sun, D. Zhang, J. Ren, Z. Wang, and J. S.Ji, "Brushstroke based sparse hybrid convolutional neural networks for author classification of chinese inkwash paintings," IEEE International Conference on Image Processing, pp. 626-630, 2015.
[13] S. Zhao, H. Yao, X. Jiang, and X. Sun, "Predicting discrete probability distribution of image emotion," IEEE International Conference on Image Processing, pp. 2459-2463, 2015.
[14] K. Peng and T. Chen, "Cross-layer features in convolutional neural networks for generic classification tasks," International Conference in Image Processing, pp. 3057-3061, 2015.
[15] W. R. Tan, C. S. Chan, H. E. Aguirre, and K. Tanaka, "Ceci n'est pas une pipe: A deep convolutional network for fine-art paintings classification," in IEEE International Conference on Image Processing ICIP, pp. 3703-3707, 2016.
[16] K.-H. Lo, K.-L. Hua, and Y.-C. F. Wang, "Depth map super-resolution via markov random fields without texture-copying artifacts," International Conference on Acoustics, Speech and Signal Processing, pp. 1414 - 1418, 2013.