Applying edit distance to hand language video

We present a revised method to compute the similarity of traditional string edit distance in this paper. Because this method lacks some types of normalization, it would bring some computation errors when the sizes of the strings that are compared are variable. In order to compute the edit distance, a new algorithm is introduced. In this paper, we solve the retrieval problem by high level features used by hand language trajectory and compare the similarity by our revised string edit distance algorithms. Trajectory based video retrieval is widely explored in recent years by many excellent researchers. Experiments in trajectory-based sign language video retrieval are presented in our paper at last, revealing that our revised edit distance algorithm consistently provide better results than classical edit distances.