Texture similarity estimation using contours

In a study of 51 computational features sets Dong et al. [1] showed that none of these managed to estimate texture similarity well and, coincidently, none of these computed higher order statistics (HOS) over large regions (that is larger than 19×19 pixels). Yet it is well-known that the human visual system is extremely adept at extracting long-range aperiodic (and periodic) "contour" characteristics from images [5, 6]. It is our hypothesis that HOS computed over larger spatial extent in the form of contour data are important for estimating perceptual texture similarity. However, to the authors' knowledge the use of contour data (rather than edge data) has not been proposed before as the basis for a set of feature vectors. We provide results of an experiment with 334 textures that shows that contour data is more important than local image patches, or 2nd-order global data, to human observers. We also propose a contour-based feature set that exploits the long-range HOS encoded in the spatial distribution and orientation of contour segments. We compare it against the 51 feature sets tested by Dong et al. [1, 2] and another contour model derived from shape recognition. The results show that the proposed method outperforms all the other feature sets in a pairs-of-pairs task and all but two feature sets in a ranking task. We attribute this promising performance to the fact that this new feature set encodes long-range HOS.