Adaptive Sparse Coding for Painting Style Analysis

Inspired by the outstanding performance of sparse coding in applications of image denoising, restoration, classification, etc, we propose an adaptive sparse coding method for painting style analysis that is traditionally carried out by art connoisseurs and experts. Significantly improved over previous sparse coding methods, which heavily rely on the comparison of query paintings, our method is able to determine the authenticity of a single query painting based on estimated decision boundary. Firstly, discriminative patches containing the most representative characteristics of the given authentic samples are extracted via exploiting the statistical information of their representation on the DCT basis. Subsequently, the strategy of adaptive sparsity constraint which assigns higher sparsity weight to the patch with higher discriminative level is enforced to make the dictionary trained on such patches more exclusively adaptive to the authentic samples than via previous sparse coding algorithms. Relying on the learnt dictionary, the query painting can be authenticated if both better denoising performance and higher sparse representation are obtained, otherwise it should be denied. Extensive experiments on impressionist style paintings demonstrate efficiency and effectiveness of our method.