Discovering Color Styles from Fine Art Images of Impressionism

Abstract

Content-based image retrieval (CBIR) has attracted much interest since the last decade. Finding painting styles from fine art images is useful for CBIR. However, little research has been done on the painting style mining. In this paper, we investigated the color style mining technique for fine art of Impressionism. Three design issues for the color style mining are the feature extraction, the feature representation, and the style mining algorithm. For the feature extraction and presentation, dominate colors, adjacent color combinations and some MPEG-7 color descriptors, are utilized to represent the color features. Above all, we utilize the spatial data structure, 2D string, to represent color layout descriptor. For the style mining algorithms, we proposed a two-stage color style mining scheme. The first stage discovers the common properties of paintings of the same style. The second stage discovers the discriminative properties among styles. The experiment on the art work of European Impressionist was conducted. The performance of effectiveness is measured by the classification accuracy of the proposed style mining scheme. The classification accuracy ranges from 70% to 90%.