The Synthesizability of Texture Examples

Dai, Dengxin; Riemenschneider, Hayko; Gool, Luc Van

doi:10.1109/cvpr.2014.387

Cited by 39 publications

(44 citation statements)

References 45 publications

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“…From the results, we can see that our method provides excellent texture exemplars based on the given natural images; they always include the Fig. 7 Patches chosen by our method and that of Dai et al [18]. dominant textures in the input images.…”

Section: Methodsmentioning

confidence: 78%

“…Firstly, we implemented the method proposed by Dai et al [18] and compared its results with those of our method, as shown in Fig. 7.…”

Section: Methodsmentioning

confidence: 99%

“…Latif-Amet et al [17] detected defects encountered in textile images and optimized results based on wavelet theory and cooccurrence matrices. Dai et al [18] evaluated the quality of a texture based on a set of target texture properties.…”

Section: Related Workmentioning

confidence: 99%

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Automatic texture exemplar extraction based on global and local textureness measures

Lyu

Wen

2018

Comp. Visual Media

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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 postprocessing. 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.

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Section: Methodsmentioning

confidence: 78%

“…Firstly, we implemented the method proposed by Dai et al [18] and compared its results with those of our method, as shown in Fig. 7.…”

Section: Methodsmentioning

confidence: 99%

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Automatic texture exemplar extraction based on global and local textureness measures

Lyu

Wen

2018

Comp. Visual Media

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“…Set14 was proposed by Zeyde et al [ZEP12], while BD100 dataset contains the 100 testing images from the Berkeley Segmentation Dataset (BSDS300) [MFTM01]. In order to further evaluate the ability of all methods for texture recovery, a highly-desired property for image super-resolution, we created a new dataset by selecting 136 diverse texture images from the ETHSynthesizability dataset [DRV14]. The dataset is named SuperTex136.…”

Section: Datasetsmentioning

confidence: 99%

Jointly Optimized Regressors for Image Super‐resolution

Dai

Timofte

Gool

2015

Computer Graphics Forum

153

105

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Learning regressors from low-resolution patches to high-resolution patches has shown promising results for image super-resolution. We observe that some regressors are better at dealing with certain cases, and others with different cases. In this paper, we jointly learn a collection of regressors, which collectively yield the smallest superresolving error for all training data. After training, each training sample is associated with a label to indicate its 'best' regressor, the one yielding the smallest error. During testing, our method bases on the concept of 'adaptive selection' to select the most appropriate regressor for each input patch. We assume that similar patches can be super-resolved by the same regressor and use a fast, approximate kNN approach to transfer the labels of training patches to test patches. The method is conceptually simple and computationally efficient, yet very effective. Experiments on four datasets show that our method outperforms competing methods.

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“…Over the past decades, tremendous investigations have been made in texture analysis, see e.g. [1][2][3][4][5][6][7], among which an active topic is developing texture models that can efficiently depict both the statistical and the geometrical aspects of textures and are robust to the variations of imaging condition as well.…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis with Shape Co-occurrence Patterns

Liu

Xia

Yang

et al. 2014

2014 22nd International Conference on Pattern Recognition

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This paper presents a flexible shape-based texture analysis method by investigating the co-occurrence patterns of shapes. More precisely, a texture image is represented by a tree of shapes, each of which is associated with several attributes. The modeling of texture is thus converted to characterize the tree of shapes. To this aim, we first learn a set of co-occurrence patterns of shapes from texture images, then establish a bag-of-words model on the learned shape co-occurrence patterns (SCOPs), and finally use the resulting SCOPs distributions as features for texture analysis. In contrast with existing work, the proposed method not only inherits the strong ability to depict geometrical aspects of textures and the high robustness to variations of imaging conditions from the shape-based texture analysis method, but also provides a more flexible way to model shape relationships (high-order statistics) on the tree. To our knowledge, this is the first time to use co-occurrence patterns of explicit shapes as a tool for texture analysis. Experiments of texture retrieval and classification on various databases report state-of-the-art results and demonstrate the efficiency of the proposed method.

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The Synthesizability of Texture Examples

Cited by 39 publications

References 45 publications

Automatic texture exemplar extraction based on global and local textureness measures

Automatic texture exemplar extraction based on global and local textureness measures

Jointly Optimized Regressors for Image Super‐resolution

Texture Analysis with Shape Co-occurrence Patterns

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