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we propose a Self-organizing Map (SOM) based framework specifically for analyzing and visualizing the relationships among artistic styles of painting collections. In our framework, we first define a set of image features based on artistic concepts; then a SOM-based hierarchical model is used to analyzing features extracted from individual artists’ painting collections. For our experiment, we obtain painting collections of six artists representing three art movements: post-impressionism, cubism and renaissance. Through our experimental results, artistic styles of different painting collections and their influential relationships can be analyzed and visualized.
If you would like to use the "large painting dataset" or the "very first" version of our feature extraction code, please cite the following publications properly:
Florence Ying Wang and Masahiro Takatsuka. SOM based Artistic Style Visualization. (ICME'13). San Jose. USA, July, 2013.
Features
- Two painting datasets. A small one representing major works of each artist, A large one representing artists' lifetime work
- Artistic image feature extraction code available
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