In a video database, the similarity between video sequences is usually measured by the percentages of similar frames shared by both video sequences, where each frame is represented as a high-dimensional feature vector. The direct computation of the similarity measure involves time-consuming sequential scans over the whole dataset. On the other hand, adopting existing indexing technique to high-dimensional datasets suffers from the "Dimensionality Curse". Thus, an efficient and effective indexing method is needed to reduce the computation cost for the similarity search. In this paper, we propose a Multi-level Hierarchical Divisive Dimensionality Reduction technique to discover correlated clusters, and develop a corresponding indexing structure to efficiently index the clusters in order to support efficient similarity search over video data. By using dimensionality reduction techniques as Principal Component Analysis, we can restore the critical information between the data points in the dataset using a reduced dimension space. Experiments show the efficiency and usefulness of this approach.