Student: Wayne, Zheng-Yun Zhuang(莊正昀) Advisor: 歐陽明

This thesis presents a new image metric that makes image querying more friendly and accurately. In fact, there are two distinct metrics, and one can mix the two distances measured by two different metrics, both from our query image to the target image, to proceed in a query. Such a mix can be applied to paint querying in image database systems, in which some user may behave as an impressionist and emphasize on the color distribution in his query while another one may like to give a sketch and draw the shape of a figure in monochrome. The result metric can be used to deal with various kinds of query images, takes little time in querying, and has a reasonable hit ratio. The experiment shows that it only takes under four seconds totally to take actions on necessary preceding operations for a 256x256x24 query, to transform it by wavelet, to operate on the database, and to search among 60 database images using our metric.

Another contribution of our thesis is to apply our image metric to video scene change detection, so as to propose a brand-new shot boundary detection algorithm rather than the other six methods that were proposed previously. This approach, which is multiresolution-based in nature, has been experimentally validated to be a better one with a higher detection hit rate.

For our research, we have built a system that not only performs traditional image database manipulation but is capable of content-based image querying. Basic operations of an ordinary image database such as augmentation, deletion, browsing, listing and modification are implemented. Enhanced functions like wavelet-based feature extraction, image registration, multiple registration, multiresolution image querying and video shot boundary detection are also presented.