Research On Content-Based Image Retrieval Technology

The thesis is focused on content-based image retrieval (CBIR). As a widely used technology, CBIR seems to be highly task-dependent. In practice, special algorithms are taken according to special application. The researches of this thesis include feature extraction, similarity measure and semantic retrieval. On the basis of it, a system prototype of CBIR is designed and some relevant experiments are carried out.On the aspect of feature extraction, color feature extraction, texture feature extraction and shape feature extraction are separately analyzed and frequently used methods are realized. On the basis of shape context, a new shape feature extraction method called Shape context by choosing the edge points self-adaptively is proposed. Experiments show that the method acquired better effect compared with shape context.On the aspect of similarity measure, the frequently used methods are realized on the basis of analyzing the concept of similarity measure. The retrieval effects of these methods are analyzed on experiments.On the aspect of semantic retrieval, the method of query vector modified is realized on the basis of analyzing frequently used semantic retrieval methods. To overcome the drawback of the above method, a new method named weighted query vector modified is proposed. Experiments show the new method is satisfactory.A system prototype of content-based image retrieval is designed and realized on the basis of it. Through the analysis of system functions, the system is divided into three modules as follows: Retrieval Parameters Setting Module, Retrieval Result Browsing Module and Feedback Module, and further introductions to these modules are made.Finally, a summary of our work are made and a prospect of the following research work are given.