Similarity grouping of paintings by distance measure and self organizing map

Abstract

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. © 2009 Springer Berlin Heidelberg.