Modelling traditional Chinese paintings for content-based image classification and retrieval

Content-based image retrieval (CBIR) has been investigated extensively in the past decade in order to classify and search images according to similarities derived from automatically extracted visual features, such as colours, textures and object shapes. It has now been realised that two fundamental problems in CBIR, namely, feature extraction and similarity measure, are likely to be domain specific. In this paper, we present some early results of applying CBIR to traditional Chinese paintings. Our research is motivated by three main goals: (1) to develop tools for art historians to study evolution and cross-influences of oriental paintings by automatically identifying visual artistic clues from digitised paintings; (2) to verify and further advance the existing CBIR techniques by limiting the images studied to a specific and simpler domain of traditional Chinese paintings; and (3) to verify and further advance the problem of high-dimensional data clustering (especially in relation to the "dimensional curse" problem). We present a framework for modelling traditional Chinese paintings, and examine various existing CBIR proposals and algorithms for their suitability for traditional Chinese paintings. In this paper, we also present a research agenda to study the problems of Chinese paintings classification and retrieval based on the framework.