Adaptive Sparse Coding for Painting Style Analysis Zhi Gao1∗, Mo Shan2∗, Loong-Fah Cheong3, and Qingquan Li4,5 1 Interactive and Digital Media Institute, National University of Singapore, Singapore 2 Temasek Laboratories, National University of Singapore 3 Electrical and Computer Engineering Department, National University of Singapore 4 The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China 5 Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China fgaozhinus,
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[email protected] Abstract. Inspired by the outstanding performance of sparse coding in applications of image denoising, restoration, classification, etc, we pro- pose 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 au- thenticity of a single query painting based on estimated decision bound- ary. Firstly, discriminative patches containing the most representative characteristics of the given authentic samples are extracted via exploit- ing 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 exclu- sively 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.