Efficient indexing is a key in content-based video retrieval solutions. In this paper we propose a new dynamic indexing scheme based on the kd-tree structure. Video sequences are first represented as traces in an appropriate low dimensional space via luminance field scaling and PCA projection. Then, the indexing scheme is applied to give the video database a manageable structure. Being able to handle dynamic video clip insertions and deletions is an essential part of this solution. At the beginning, an ordinary kd-tree is created for the initial database. As new video traces are added to the database, they will be added to the indexing tree structure as well. A tree node will be split if its size exceeds a certain threshold. If the tree structure un-balance level exceeds a threshold, merging and re-splitting will be performed. Preliminary experiments showed that merging and re-splitting will ensure the efficiency of the indexing scheme.
['Li Gao', 'Zhu Li', 'Aggelos K. Katsaggelos']
A Kd-Tree Based Dynamic Indexing Scheme for Video Retrieval and Geometry Matching