A visual analytics framework for discovering temporal patterns in large scale artistic collections
Abstract
In this research work we have come up with a framework for the visual analysis of temporal large scale artistic images. The goal of the research paper is to understand and discover the patterns in collections of art. The key technical aspect will be to adapt a standard model/framework and applying it on the specific art collection. Spatial consistency between different feature matches will be used. This will lead to having a more accurate style-invariant matching, and identification of patterns. The approach will be evaluated on selected artistic images. The findings have given us the understanding as to how the visualization of the large-scale paint image dataset can be made while finding relationships among different paintings through similarities and semantic information. This included tracking patterns, finding features with elements and principles in images by using image features. These observations have led to the understanding of influences of artists on each other based on patterns and origins of the paintings.
References
[2] L. A. Gatys, A. S. Ecker, and M. Bethge. “Image style transfer using convolutional neural networks”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2414–2423, 2016.
[3] Hertzmann, Aaron. "Can computers create art?." In Arts, vol. 7, no. 2, p. 18. MDPI, 2018.
[4] Picard, David, Philippe-Henri Gosselin, and Marie-Claude Gaspard. "Challenges in content-based image indexing of cultural heritage collections." IEEE Signal Processing Magazine 32, no. 4 (2015): 95-102.
[5] Strezoski, Gjorgji, and Marcel Worring. "Omniart: multi-task deep learning for artistic data analysis." arXiv preprint arXiv:1708.00684 (2017).
[6] Tan, Wei Ren, Chee Seng Chan, Hernán E. Aguirre, and Kiyoshi Tanaka. "Ceci n'est pas une pipe: A deep convolutional network for fine-art paintings classification." In 2016 IEEE international conference on image processing (ICIP), pp. 3703-3707. IEEE, 2016.
[7] Ginosar, Shiry, Daniel Haas, Timothy Brown, and Jitendra Malik. "Detecting people in cubist art." AI Matters 1, no. 3 (2015): 16-18.
[8] Aubry, Mathieu, Bryan C. Russell, and Josef Sivic. "Painting-to-3D model alignment via discriminative visual elements." ACM Transactions on Graphics (ToG) 33, no. 2 (2014): 1-14.
[9] Westlake, Nicholas, Hongping Cai, and Peter Hall. "Detecting people in artwork with cnns." In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part I 14, pp. 825-841. Springer International Publishing, 2016.
[10] Seguin, Benoit, Isabella diLenardo, and Frédéric Kaplan. "Tracking Transmission of Details in Paintings." In DH. 2017.
