Content-based Image Retrieval Technology Research

Content-based Image Retrieval (CBIR) technology made uses of image content features (color, texture, shape, spatial relations and etc.) which are analyzed and extracted automatically by computer to achieve the effective retrieval. In the meantime, CBIR conquered the defects of traditional text-based image retrieval technology, such as strong subjectivity and heavy workload. However, this technology still encounters much difficulty which is generated by the semantic gap between image semantic features and the lower features, resulting in the fact that the extracted content features still mainly centered upon the lower features such as color, texture and shape. Therefore, in a long run, it is still an unsolved problem about how to integrate with semantic features to achieve better connection between the lower physical features and the image content for effective retrieval.Based on an overall analysis about the CBIR technology, this thesis offered three new image retrieval methods: stereometry feature-based image retrieval(SBIR), entropy &fractal vector-based image and rough set-based correlative feedback image retrieval(RCFBIR).In the system of SBIR, firstly, extracted the image's stereometry feature on the analogy of the image's stereometry model, then denoted this feature with quadtree indexed structure, last carried out the grade image retrieve; E&FBIR illustrated the image with image information entropy and fractal vector so as to realize the dynamic image retrieval. At the same time, RCFBIR, taking the rough set theory in the retrieval system, constructing decision table on the basis of user's semantic feedback and realizing image effective retrieval, greatly improved the current correlative feedback-based image retrieval.In the end, the thesis took an experimental system into action with multi-feature retrieval for algorithm validity verification.