Identifying painters from color profiles of skin patches in painting images

Research on digital analysis of painting images has received very little attention. The exact nature of scientific methods seems to be antithesis of art. Nevertheless, several papers have proposed methods to bridge this gap and have obtained interesting results. In fact, some art theorists themselves have pointed out the usefulness of specific quantifiable features in the paintings. This thesis presents a method for identifying painters using color profiles of skin patches in painting images. A model of color profiles and its feature extraction method are proposed. Various color models for representing the color profiles are explored. Moreover, various implementations of multiclass Support Vector Machine classifiers are compared for their classification performance. Experimental results show that a weighted combination of several Directed Acyclic Graph SVMs with Gaussian kernels gives the best classification performance.