Research on the application of image processing technology based on SIFT features extraction in the retrieval and classification of art works

Along with the development of image processing and network technology, more and more art works were digitalized and exhibited on the internet. In this paper, we proposed two artistic style classification schemes based on the SIFT features: the topic model and the pyramid match model. The pyramid match model extracts SIFT features of the painting, and let these low level features represent the style and learns a codebook of the visual style, and use sparse coding to finding succinct representations for artistic style. It uses pyramid matching as a way of measurement of the style similarity between art works and trains a linear Multi-class SVM model to predict which artistic style the art work belongs to. Different from the discriminative model, in order to complete the classification task, the topic model use the images’ topic distribution getting from the topic learning procedure as the input to train the classifier.