Dominant Texture and Diffusion Distance Manifolds

Abstract

Texture synthesis techniques require nearly uniform texture samples, however identifying suitable texture samples in an image requires significant data preprocessing. To eliminate this work, we introduce a fully automatic pipeline to detect dominant texture samples based on a manifold generated using the diffusion distance. We define the characteristics of dominant texture and three different types of outliers that allow us to efficiently identify dominant texture in feature space. We demonstrate how this method enables the analysis/synthesis of a wide range of natural textures. We compare textures synthesized from a sample image, with and without dominant texture detection. We also compare our approach to that of using a texture segmentation technique alone, and to using Euclidean, rather than diffusion, distances between texture features.

Automatic texture exemplar extraction based on global and local textureness measures

“…For dominant texture extraction, Lu et al [6] take 18 minutes to process a 125 × 94 image. Although Wang and Hua [7] and Moritz et al [5] give real-time dominant texture extraction algorithms, they require the target textures to covering most of the image.…”

“…Lu et al [6] first employed diffusion distance manifolds to identify the dominant textures in an input image, but their method is quite time-consuming, taking about 18 minutes to process an image of size 125×94. Wang and Hua [7] proposed a faster dominant texture extraction algorithm based on multi-scale hue-saturation-intensity histograms, but it may fail when the main colors in the dominant texture and the outliers are similar.…”

“…However, these dominant texture extraction algorithms usually require the target textures to cover the majority of the image, as shown by the results in Refs. [5][6][7][8].…”

“…They typically pay insufficient attention to obtaining ideal exemplars from natural images, and little work on automatic texture exemplar extraction is reported in the literature [4,5]. Although several algorithms have been proposed for extracting dominant textures from an image [6][7][8], automatic texture exemplar extraction systems for synthesis applications are still lacking. Artists typically can only acquire exemplars manually by a process of image cropping and careful post-processing, which is both labor intensive and tedious, especially when many exemplars are needed to create complex virtual scenes.…”

Ortho-diffusion decompositions of graph-based representation of images

“…It was shown to have the ability of handling non-symmetric neighborhoods, [15]. Diffusion wavelets have been used in representation learning [16], planning [17], document corpora analysis [18], image segmentation [19], texture synthesis [20], image sequence analysis [21], 3D mesh compression [22], and for 3D shape retrieval in [23].…”