Multivariate image similarity in the compressed domain using statistical graph matching

We address the problem of image similarity in the compressed domain, using a multivariate statistical test for comparing color distributions. Our approach is based on the multivariate Wald–Wolfowitz test, a nonparametric test that assesses the commonality between two different sets of multivariate observations. Using some pre-selected feature attributes, the similarity measure provides a comprehensive estimate of the match between different images based on graph theory and the notion of minimal spanning tree (MST). Feature extraction is directly provided from the JPEG discrete cosine transform (DCT) domain, without involving full decompression or inverse DCT. Based on the zig-zag scheme, a novel selection technique is introduced that guarantees image's enhanced invariance to geometric transformations. To demonstrate the performance of the proposed method, the application on a diverse collection of images has been systematically studied in a query-by-example image retrieval task. Experimental results show that a powerful measure of similarity between compressed images can emerge from the statistical comparison of their pattern representations.