Study On Key Techniques In Automatically Extracting Semantic Information From Visual Media

In the past few years, techniques of computers and Internet are improving very fast. This causes the amount of image and video content increasing drastically, and more and more people could conveniently access various equipments to obtain the desired visual information. Information era as the core of multimedia information services is coming to us. Techniques to process and analyze visual information need to be constructed to meet various application demands of users on images and video clips. However,"Semantic Gap"is a great challenge for human and computer harmonious interaction; this is because low-level features used by computers could not be always interpreted to high-level concepts that are commonly used by human. Although there exist many research works on semantic analysis and understanding of visual media, this important research area is far from satisfactory, as users need more automatically extracted semantic information.In this thesis, we make a study on some key techniques in automatically extracting semantic information from visual media. A four-level technical framework for semantic extraction is proposed, including object semantic layer, scene semantic layer, knowledge and emotion semantic layer, and semantic application layer. Four kinds of key techniques are investigated respectively: object detection, scene classification, high-level concept extraction and ontology-based semantic application. It is hard to provide a general solution to extract all the semantic concepts from visual media, and is best approached by a divide-and-conquer strategy. Sports video always appeals to large audiences, automatically extracting useful semantic information from sports video to facilitate retrieval and organization is an important problem; and this has emerged as a hot research area recently. With the Beijing 2008 Olympic Games being near; research on semantic understanding of sports video has a special meaning for China. On the other hand, automatically analyzing and understanding digitized art images and extracting their type, style and other semantic information is an important and imperative problem that needs to be addressed. Traditional Chinese Painting is the gem of of Chinese traditional arts; research on this kind of art images is also an important problem. This thesis investigates on extracting semantics from visual media including video and image content; particularly, we concentrate on sports video and art images. At the end, we propose a technique to classify night scene images, which is also an important problem in semantic processing of images. The contributions of the thesis are as follows:1) Firstly, we perform a global analysis on system framework of automatically extracting semantic information from visual media. This is a necessary work, as on...