Improving the selectivity of range query for image databases based on a probabilistic framework

Estimating the selectivity of range query when applied to image database is an important stage for optimizing a image database query. In this paper, we propose a new approach to approximate the selectivity of a range query based on a probabilistic framework. Specifically, each image is represented as a sequence of states in a multi-dimensional space, where each dimension corresponds to a particular time stamp. We then associate a histogram with each dimension, and the histogram bins in each dimension correspond to specific states. A range query in the proposed probabilistic framework is the sum of the probabilities of observing a sequence of states within a hyper-sphere. A minimum bounding box and a maximum enclosed box are used to determine the upper bound and the lower bound of the selectivity of the range query. The experiments show that this probabilistic framework works well for an image database.