Automatic Annotation Of Semantic Image Retrieval Based On The Contents Of Subjective Mapping

As a kind of important data information, the application of image is paid more and more attention. How to search the image fast and effectively has become an urgent problem which needs to be solved at present with larger and much abundant image resource.There is a great deal of complex calculation in the process of traditional content-based image retrieval, in which the subjective retrieval desire of users can not be reflected exactly. However, the problem can be solved by semantic feature-based technology. But the abstracting of the semantic is still an unsolved difficult question till now. Hence, on the basis of some existing mechanisms, a novel mechanism named "the automatic semantic annotation image retrieval based on content subjective mapping" is proposed in this paper. It combines the low level visual feature and high level semantic feature, and makes use of classification with similarity of objective or backgrounds to reconstructs picture resource. It solves the relative problems of putting semantic on image by the way of automatic semantic annotation, and the simulation results prove the proposed mechanism in this paper can improve the performance of retrieval.The works of this paper are as follows:At first, we study the low level visual feature of picture, including color, texture and shape. On the basis of analyzing each feature's theories, we discuss the extraction algorithm and similarity measurement. To evaluate the effective of the algorithms which are proposed in this paper, we simulate these algorithms and analyze the corresponding results.Second, we study the semantic annotation based on extracting visual feature of image effectively. In this paper, the implement of this part includes three detail sections: subjective mapping, automatic semantic annotation and relevance feedback. The subjective mapping is the premises of offering the automatic semantic annotation an exercise set of studying and realization. It also can put the initialized semantic on image. There are two cases in automatic semantic annotation: one is to study the semantic of the highest similarity image information in the exercise set and complete the semantic annotation automatically after a new one being processed; the other one is to modify the initialized semantic annotation with the feedback information of users. The retrieval mode can make the semantic annotation more and more exact, and improves the performance of semantic annotation by relevance feedback of users'. It makes the contained semantic much closer to the image comprehension of users and reach the final purpose of improving the retrieval capability effectively.At the end of this paper, we implement the retrieval which is proposed in this paper, including the environment of simulations, the whole structure and realization flow. And the simulation results prove that the proposed mechanism is effective...