Content-Based Image Retrieval In Scientific Databases

With the development of scientific databases, more and more multimedia information are incorporated, which includes images, audio, video, etc. Scientific databases are facing big challenge on how to get relevant information from the vast multimedia information repository. By using the technologies of image processing, pattern recognition, computer vision and database, this dissertation studies some key problems in the field of image retrieval. This dissertation researches on new methods of image features extraction, image retrieval and feature vectors indexing under scientific database environment; on the other hand, due to the distributed nature of scientific database, we discussed several models of distributed image retrieval system, furthermore some issues related to distributed image retrieval are deeply studied. The contributions of the dissertation are as follows:1) A new color image feature extraction method is proposed, which can successfully extract neighboring main color and its topologies. This kind of feature not only uses the color feature but the spatial relationship between different main colors. In comparison, this feature extraction method do not involve complex image segmentation processes, it's simple and useful.2) The classical color histogram has its drawbacks; it does not contain information about the spatial distributions of pixels in an image. This dissertation present a new weighted color histogram feature, it can integrate the pixels spatial distribution information into the color histogram. Thus retain the advantages of classical color histogram such as simple and invariant to viewpoint changes; at the same time, can substantially improve the recall ratio. As a fundamental retrieval method, it can easily be integrated with other methods.3) Typically, the scale of image databases in scientific database are very large, it is more attractive to quickly retrieve a rough result set than to spend a lot of time to get a precious result. According to this point of view, a vector indexing method called locality sensitive hashing is proposed. This method can effectively prune super large searching space; reduce time complexity of similar search process. The experiment results show that this method is still effective when the data scale is very large, and it has superior scalability than traditional tree-structured indexing methods.4) Under the scientific database environment, image data sources are heterogeneous and distributed, image retrieval system needs distributed architecture.