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• W2216442425 abstract "Inspired by the outstanding performance of sparse representation (SR) in a variety of image/video relevant classification and identification tasks, we propose an adaptive SR method for painting style analysis. Significantly improved over previous SR-based methods, which heavily rely on the comparison of query paintings, our method is able to authenticate or classify a single query painting based on the estimated decision boundary. Specifically, discriminative patches containing the most representative characteristics of the given samples are first extracted via exploiting the statistics of their representations on the discrete cosine transform (DCT) basis. Then, the strategy of adaptive sparsity constraint is enforced to make the dictionary trained on such patches more adaptive to the training samples than via previous SR techniques. Applying the learned dictionary, the query painting can be authenticated if both better denoising performance and higher kurtosis are obtained compared to the baseline estimated via applying the DCT basis; otherwise, it should be denied. Extensive experiments on our dataset comprised of paintings from van Gogh, his contemporaries, the Wacker forgery, and Monet demonstrate the effectiveness of our method." @default.
• W2216442425 created "2016-06-24" @default.
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• W2216442425 date "2015-08-14" @default.
• W2216442425 modified "2023-09-25" @default.
• W2216442425 title "Adaptive Sparse Representation for Analyzing Artistic Style of Paintings" @default.
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• W2216442425 doi "https://doi.org/10.1145/2756556" @default.
• W2216442425 hasPublicationYear "2015" @default.
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