Identifying the painter using background texture features and neural networks
We test if we can distinguish the works of two impressionist painters using features derived only from the background of their paintings. The background area is said to bear the unconscious habitual movements of the artist compared to areas where there is fine detail (foreground). We derive Gray Level Co-occurrence Matrix (GLCM) features from sky and cloud elements of Claude Monet and Edouard Manet's paintings and apply neural networks on duplets and triplets of features. From 6-fold validation the neural network was able to separate the works of these artists to an accuracy of 86% for the duplet of features and 84% for the triplet of features.
By submitting their manuscript to the Samahang Pisika ng Pilipinas (SPP) for consideration, the Authors warrant that their work is original, does not infringe on existing copyrights, and is not under active consideration for publication elsewhere.
Upon acceptance of their manuscript, the Authors further agree to grant SPP the non-exclusive, worldwide, and royalty-free rights to record, edit, copy, reproduce, publish, distribute, and use all or part of the manuscript for any purpose, in any media now existing or developed in the future, either individually or as part of a collection.
All other associated economic and moral rights as granted by the Intellectual Property Code of the Philippines are maintained by the Authors.