ABSTRACT
This chapter proposes an automatic classification method of artistic images based on the features computation, which can classify and visualize artistic images effectively. The clustering mechanism based on MLAP is used to classify images hierarchically. As a result, experiments show the effectiveness of this proposed method for image classification. With the development of image acquisition hardware devices and computer graphics technique, many of the artistic paintings have been digitized into images for appreciation, communication, dissemination, etc. The visual similarity of images is incorporated into our proposed approach to generate hierarchical clustering organization, which can provide users with a hierarchical browsing structure of massive artistic images with the semantics of public aesthetics. The theme, content, colors, genre, and drawing skills of the artistic image are important features for the visual distinguishing of images. The Local Binary Pattern is an image feature operator that is used to describe the local texture, with an advantage of grayscale invariance, rotational invariance, etc.
