An Ontology-Based Image Retrieval System

Images constitute an important part of contents of web pages on the Internet, as they can represent information in a direct and vivid manner. The amount of image information is rapidly raising due to digital cameras and mobile telephones equipped with such devices. Meanwhile, the application of image information is becoming more and more wide, which leads to more and more strong demand for multimedia data such as image. The traditional image retrieval technologies such as text-based method couldn't satisfy people's demand. Content-based approach is able to index automatically an image with low-level features extracted from the image, but low-level features used by it always could not be interpreted to high-level concepts that are commonly comprehended by human..Ontology theory has been drawing more and more research attention of information retrieval field in recent years. This thesis firstly introduces ontology theory and related work of its application to image retrieval, then a general-purpose ontology model used to describe images comprehensively is built, with which we can describe different aspects of image information.In order to automate the extraction of high-level semantic feature of image, this paper introduces the basic theory of support vector machine (SVM) and employs SVM to map low-level features of images to high-level semantic concepts. With this method, we can annotate an image in an automatic way.Besides, different users may have different interpretations on a same image. In order to capture users' different understandings of images, this paper builds a semantic structure that can be updated according to users' feedback, and a retrieval algorithm combining low-level features with texts was proposed.At last, an experimental image management and retrieval system has been implemented, which provides three types of image retrieval methods. An experiment was conducted on this system and the result shows that the retrieval algorithm combining low-level features with texts can improve the efficiency of image retrieval.