Use of orthogonal arrays to aid relevance feedback in content based image retrieval systems

Abstract

Supervised learning algorithms (relevance feedback (RF) algorithms) are often used in content based image retrieval (CBIR) systems to enhance interactive search and browsing of image databases. One of the issues associated with RF based CBIR systems is the lack of a large training set. Labeling of images is a time consuming activity and user's usually do not have the patience to provide feedback on a large set. Thus the challenge is to select a "good" small training set in order to improve the retrieval performance of CBIR systems. In this paper we propose to use orthogonal arrays (OA), popularly used in design of experiments, in order to select this set. The property of OA that make them useful for CBIR systems is that they can be designed with or without using any prior classification information. We show that the top k retrieval accuracy increases rapidly as the number of labeled samples increase. © 2005 IEEE.