Applying perceptually based metrics to textural image retrieval methods

Texture plays an important part in many Content Based Image Retrieval systems. This paper describes the results from a human study, which asked 30 volunteers to classify images from the Brodatz Textures album. We use these results to derive a subset which show good agreement among the different individuals. The results for this subset were used to evaluate the retrieval performance of a range of statistical, Fourier-based, and spatial/spatial filtering methods. However, no one computational method works well for all textures, unlike the human visual system. We show how each of the ten methods correlates with the rankings from the human studies.the results typically match for only about 20%-25% of the images. Combining two techniques can improve the retrieval performance, as judged by human users. We also identify a further subset of the Brodatz images where no computer method correlates significantly with the composite human ranking. Of the 85 images selected by the human study, only 64 have any significant correlation with one or more of the computational methods in this paper. The excluded images, where human users agree with each other, but none of the methods we evaluated did, provide a further challenge to texture-based image retrieval techniques.