Semantic Retrieval Technology And Applications For Ancient Murals

Ancient murals as represented by the Dunhuang murals, provide the researchers with rich and vital resources for studying religion, history, geography, art, folk customs and costumes etc. The development of ancient murals digitization makes it possible for computerized retrieval. However, the present image retrieval technologies have difficulties in retrieving ancient murals, since they lack of the abilities to handle complex semantic and features of layout in painting.Aiming at the above-mentioned problems, this thesis discusses the essential techniques of mural contents representation and measuring, and proposes a novel semantic retrieval approach for ancient murals. The real applications in Dunhuang murals show the efficiency and effectiveness of our approach.The research of this thesis includes the following topics:1. Review of the development tendency of semantic image retrieval is given. Furthermore, text information retrieval, image completion technologies are introduced, which provide ideas and inspiration for this thesis.2. A relevance ranking model for relevance evaluation is proposed. The relevance ranking model measures the relevance of mural images from three aspects which are layout, semantics and topic. To the best of our knowledge, this is the first time the layout features in the view of painting are taken into similarity measure.3. Topic based retrieval and scene based retrieval are proposed for murals retrieval. By query expansion, topic based retrieval optimizes query according to its semantics and topic. Then, the results are sorted by the relevance ranking model. With the aid of domain knowledge, scene based retrieval converts query into the real intention of user. Then, a fast content indexing schema based on encoding is proposed to speed up the scene distinguish and extraction process. Finally, the results scenes are sorted by a comprehensive ranking mechanism.4. Based on the work above, a prototype system is designed and implemented for ancient murals research and preservation. In the case of ancient murals research, semantic retrieval technology is applied to murals classification. Our method can avoid the missing of important murals and improve the efficiency. By applying semantic retrieval technology to the virtual completion of ancient murals, the most relevant images with similar sculpt and style are selected as candidate images. Then, ASM, PCA and region blending method are used to produce a seamless completion.The applications in Dunhuang murals show that our method can be applied on the real applications for ancient murals research and preservation. In future, we will also explore the mural-scriptures cross-media retrieval, the association analysis of mural images and the evolution of mural layout.