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Research Article | Open Access

Automatic texture exemplar extraction based on global and local textureness measures

Shenzhen University, Shenzhen 518000, China.
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Abstract

Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques emphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, obtaining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant post-processing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly crop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured patches from non-textured patches, in conjunction with SVM prediction. To identify real texture exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure (using binary Gabor pattern (BGP)) and dominant color features (using color histograms) between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures. We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures manually selected by an artist and a statistical study demonstrate its effectiveness.

Keywords

texture exemplar extractiontexturenessGIST descriptorbinary Gabor pattern (BGP)

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Computational Visual Media
Volume 4 Issue 2,
June 2018
Pages 173-184
DOI: 10.1007/s41095-018-0106-z
Cite this article:
Wu H, Lyu X, Wen Z. Automatic texture exemplar extraction based on global and local textureness measures. Computational Visual Media, 2018, 4(2): 173-184. https://doi.org/10.1007/s41095-018-0106-z

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Revised: 25 December 2017
Accepted: 31 December 2017
Published: 17 March 2018
© The Author(s) 2018

This article is published with open access at Springerlink.com

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