Content-based image retrieval using joint correlograms

The comparison of digital images to deter-mine their degree of similarity is one of the fundamental problems of computer vision. Many techniques exist which accomplish this with a certain level of success, most of which involve either the analysis of pixel-level features or the segmentation of images into sub-objects that can be geometrically compared. In this paper we develop and evaluate a new variation of the pixel feature and analysis technique known as the color correlogram in the context of a content-based image retrieval (CBIR) system. Our approach is to extend the autocorrelogram by using multiple image features in the same way the joint histogram extends the color histogram. The experiment shows that the joint correlogram indexing method gives a significant improvement over histogram or color-only correlogram indexing, and it is also memory-efficient.