Art Works Retrieval And Classification

Along with the development of image processing and network technology, more and more art works were digitalized and exhibited on the internet. How to effectively organize and manage the works so that people can browse and retrieve them more easily has become an imperative problem need to be addressed.This thesis summarized content based image retrieval and image classification technology, such as visual feature extraction, metric space based high-dimensional data index technology, sparse coding, image classifier. With regard to the characters of art works, this thesis proposed and designed an image retrieval system based on text and content image retrieval technology.Artistic style, as the visual features of art works, represents the high level semantics of artifacts. Classification of artistic style depends mainly on human knowledge and experience. Due to the huge number of art works, classifying artistic style of art works by hand becomes infeasible. In this thesis, we proposed two artistic style classification schemes based on the SIFT features:the topic model and the pyramid match model. The pyramid match model:1) extract SIFT features of the painting, and let these low level features represent the style; 2) learn a codebook of the visual style, and use sparse coding to finding succinct representations for artistic style; 3) use pyramid matching as a way of measurement of the style similarity between art works; 4) train 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.