Video data indexing by 2D C-trees

Video data contains a large amount of spatial and temporal information. The changes of video frames are quite useful for motion analysis and cannot be provided by other media easily. Chang et al. had proposed an effective 2D string approach for spatial indexing of image data. In this paper, we extend this iconic approach and apply it to video data indexing. We propose 2D C-trees to represent the spatial content within individual frames. A video sequence can then be represented and indexed by a temporal set or an ordering set of 2D C-trees. The similarity retrieval of video matching problem becomes the problem of video sequence matching by computing the similarity, or the minimum cost of matched frames. We present three schemes, full-sequence matching, segment matching and subsequence matching, for video information retrieval. The matching schemes can be modified and extended to approximate sequence matching by computing the partial distance for providing a comprehensive retrieval of video data. A prototype video information system is also developed to validate the effectiveness of video data indexing by 2D C-trees.