Efficient Region-Based Image Retrieval

Abstract

Region-based image retrieval(RBIR) was recently proposed as an extension of content-based image retrieval(CBIR). An RBIR system automatically segments images into a variable number of regions, and extracts for each region a set of features. Then, a dissimilarity function determines the distance between a database image and a set of reference regions. Unfortunately, the large evaluation costs of the dissimilarity function are restricting RBIR to relatively small databases. In this paper, we apply a multi-step approach to enable region-based techniques for large image collections. We provide cheap lower and upper bounding distance functions for a recently proposed dissimilarity measure. As our experiments show, these bounding functions are so tight, that we have to evaluate the expensive distance function for less than 0.5\%of the images. For a typical image database with more than 370,000images, our multi-step approach improved retrieval performance by a factor of more than5 compared to the currently fastest methods.