Study On Multi-dimensional Index Technique Of Content-based Retrieval In Image Database

Along with the research and development of computer technology, image database has more and more applications in many domains, such as medicine and brand image storehouse, digital library and so on .In order to find the wanted image in the large capacity of image database, the most effective method is content-based retrieval, which needs to extract the corresponding feature of the images and store them as multi-dimensional vector database. multi-dimensional vector database is used in so many domains as the computer graphics, the geographic information system,the multimedia database and so on. For fast retrieval, it must draw support from effective index. Studied on the development process of multi- dimensional index technology and every kind of index structure, the main research and innovation is concerned with the following two methods.The first method is to make index directly on the multi- dimensional vector database. Different space or data demarcation make different index structure. In order to reduce the overlaps and enhance the index efficiency as far as possible, we deeply researched and assay the typical index structure X-Tree, SS-Tree and SR-Tree, and introduce the super node idea in X-Tree to make up for the split algorithm of SR-Tree. A new index structure is designed. Experiments show the method can effectively enhance the index efficiency.The other method is to make some transform, reflection or vector compression to the multi-dimensional data at first, and then make index structure. In order to reduce the I/O number and CPU time, we introduce angle information to NB-Tree to make up for the shortcomings. Experiments show the method can effectively enhance the indexing performance.