Content-based image database retrieval using variances of gray level spatial dependencies

Abstract

In this paper, we discuss how we use variances of gray level spatial dependencies as textural features to retrieve images having some section in them that is like the user input image. Gray level co-occurrence matrices at five distances and four orientations are computed to measure texture which is defined as being specified by the statistical distribution of the spatial relationships of gray level properties. A likelihood ratio classifier and a nearest neighbor classifier are used to assign two images to the relevance class if they are similar and to the irrelevance class if they are not. A protocol that involves translating a K x K frame throughout every image to automatically construct groundtruth image pairs is proposed and performance of the algorithm is evaluated accordingly. From experiments on a database of 300 512 x 512 grayscale images with 9,600 groundtruth image pairs, we were able to estimate a lower bound of 80% correct classification rate of assigning sub-image pairs we were sure were relevant, to the relevance class. We also argue that some of the assignments which we counted as incorrect are not in fact incorrect.