Near-duplicate video detection is becoming a core-technology for analyzing the structure of a large-scale video archive. It, however, is naturally an O(n²) problem, where n is a value proportional to the total length of an input video stream. We have previously challenged this time-consuming task by reducing the cost required for each of the O(n²) comparisons. This paper, on the other hand, proposes a method that reduces the number of comparisons by adaptively dividing the feature space according to the distribution of feature points.