Texture Similarity Estimation Using Contours

Xinghui Dong and Mike Chantler

In Proceedings British Machine Vision Conference 2014


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.


Poster Session


Extended Abstract (PDF, 1 page, 277K)
Paper (PDF, 11 pages, 522K)
Bibtex File


Xinghui Dong, and Mike Chantler. Texture Similarity Estimation Using Contours. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.


	title = {Texture Similarity Estimation Using Contours},
	author = {Dong, Xinghui and Chantler, Mike},
	year = {2014},
	booktitle = {Proceedings of the British Machine Vision Conference},
	publisher = {BMVA Press},
	editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
	doi = { http://dx.doi.org/10.5244/C.28.71 }