Painting image classification based on aesthetic style similarity rule

For the problem of the traditional classification methods that often directly construct descriptors and ignoring the inherent properties of aesthetic style, starting from human brain cognitive mechanisms, we propose to describe the aesthetic style on the basis of the feature vector and similarity principle. We create aesthetic style similarity rule (ASSR) based on similarity principle and deep analysis of the inherent properties of aesthetic style. Follow ASSR, we then quantify style features that are generally accepted in realm of art to build the image self-similarity descriptor. The distance function is used in our method to compute the similarity coefficient between one image to all other images. Finally, the similarity matrix composed of similarity coefficients could be treated as the aesthetic style similarity descriptor for the image, and Adaboost classifier is taken to evaluate the unknown images. The results demonstrate the efficiency of ASSR and our method for the painting image classification with different aesthetic style obtains good performance.