A Simple but Efficient Scheme for Colour Image Retrieval Based on Statistical Tests of Hypothesis

This paper proposes a simple but efficient scheme for colour image retrieval, based on statistical tests of hypothesis, namely test for equality of variance, test for equality of mean. The test for equality of variance is performed to test the similarity of the query and target images. If the images pass the test, then the test for equality of mean is performed on the same images to examine whether the two images have the same attributes / characteristics. If the query and target images pass the tests then it is inferred that the two images belong to the same class i.e. both the images are same; otherwise, it is assumed that the images belong to different classes i.e. both the images are different. The obtained test statistic values are indexed in ascending order and the image corresponding to the least value is identified as same / similar images. The proposed system is invariant for translation, scaling, and rotation, since the proposed system adjusts itself and treats either the query image or the target image is sample of other. The proposed scheme provides cent percent accuracy if the query and target images are same, whereas there is a slight variation for similar, transformed.