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Author Gao, Zhi ♦ Shan, Mo ♦ Li, Qingquan
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword DCT baseline ♦ Sparse representation ♦ Dictionary learning ♦ Discriminative patches
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.
ISSN 15564673
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-08-01
Publisher Place New York
e-ISSN 15564711
Journal Journal on Computing and Cultural Heritage (JOCCH)
Volume Number 8
Issue Number 4
Page Count 15
Starting Page 1
Ending Page 15

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Source: ACM Digital Library