EBS k-d Tree: An Entropy Balanced Statistical k-d Tree for Image Databases with Ground-Truth Labels

Abstract

In this paper we present a new image database indexing structure — Entropy Balanced Statistical (EBS) k-d Tree. This indexing mechanism utilizes the statistical properties and ground-truth labeling of image data for efficient and accurate searches. It is particularly valuable in the domains of medical and biological image database retrieval, where ground-truth labeling are available and archived with the images. The EBS k-d tree is an extension to the statistical k-d tree that attempts to optimize a multi-dimensional decision tree based on the fundamental principles from which it is constructed. Our approach is to develop and validate the notion of an entropy balanced statistical based decision tree. It is shown that by making balanced split decisions in the growth processing of the tree, that the average search depth is improved and the worst case search depth is usually dramatically improved. Furthermore, a method for linking the tree leaves into a non-linear structure was developed to increase the n-nearest neighbor similarity search accuracy. We have applied this to a large-scale medical diagnostic image database and have shown increases in search speed and accuracy over an ordinary distance-based search and the original statistical k-d tree index.