Abstract
Motion is the major feature that differentiates a video from a still image. In content-based retrieval, the motion track of a symbol object can be used as an index of video databases. Query processing based on such index can be treated as a curve matching problem. Two important criteria for solving the problem are sub-matching and approximate-matching. In this thesis, two query processing approaches are proposed. The first approach expands the motion track index to a region and allows a user to query against this region. In the second approach, both the motion track index and the query curve are modeled as a combination of peaks and each peak is coded according to its orientation, angle and temporal informaiton. The curve matching problem is then converted into a string matching problem, which is solved efficiently by a new finite automata based method. The proposed string matching algorithm is also shown to be scalable to the growth of the size of the database.