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High time cost is the bottle-neck of video scene segmentation. In this paper we use a heuristic method called sort-merge feature selection to construct automatically a hierarchy of small subsets of features that are progressively more useful for segmentation. A novel combination of fastmap for dimensionality reduction and Mahalanobis distance for likelihood determination is used as induction algorithm. Because these induced feature sets from a hierarchy with increasing classification accuracy, video segments can be segmented and categorized simultaneously in a coarse-fine manner that efficiently and progressively detects and refines their temporal boundaries. We analyze the performance of these methods, and demonstrate them in the domain of long (75 minute) instructional video.