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2009 | OriginalPaper | Chapter

Similarity Grouping of Paintings by Distance Measure and Self Organizing Map

Authors : Naohiro Ishii, Yusaku Tokuda, Ippei Torii, Tomomi Kanda

Published in: Knowledge-Based and Intelligent Information and Engineering Systems

Publisher: Springer Berlin Heidelberg

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Paintings have some sensibility information to human hearts. It is expected in paintings to process such sensibility information by computers effectively. For appreciation of paintings, grouping of paintings with similar sensitivity will be helpful to visitors as in painting gallery. In this paper, we developed a distance measure to group and classify similar paintings. Further, we applied the self organizing method (SOM) by two layered neural network to classify paintings. Then, the attributes of the sensibility of paintings are checked first. Next, color attributes of paintings are also checked. Paintings data with these attributes were computed by applying these techniques. Relatively well grouped results for the classification of paintings were obtained by the proposed method.

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Metadata
Title
Similarity Grouping of Paintings by Distance Measure and Self Organizing Map
Authors
Naohiro Ishii
Yusaku Tokuda
Ippei Torii
Tomomi Kanda
Copyright Year
2009
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-04592-9_88

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