What Are Textons?

Zhu, Song Chun; Guo, Cheng-en; Wu, Ying; Wang, Yizhou

doi:10.1007/3-540-47979-1_53

Cited by 35 publications

(17 citation statements)

References 11 publications

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“…Stroke textons [31], which are referred to fundamental micro-structures in natural images, reflect perceptual style patterns. Methods such as [8,23] are dedicated to learn stroke control in transfer process.…”

Section: Introductionmentioning

confidence: 99%

Attention-Aware Multi-Stroke Style Transfer

Yao

Ren

Xie

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

165

126

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Neural style transfer has drawn considerable attention from both academic and industrial field. Although visual effect and efficiency have been significantly improved, existing methods are unable to coordinate spatial distribution of visual attention between the content image and stylized image, or render diverse level of detail via different brush strokes. In this paper, we tackle these limitations by developing an attention-aware multi-stroke style transfer model. We first propose to assemble self-attention mechanism into a style-agnostic reconstruction autoencoder framework, from which the attention map of a content image can be derived. By performing multi-scale style swap on content features and style features, we produce multiple feature maps reflecting different stroke patterns. A flexible fusion strategy is further presented to incorporate the salient characteristics from the attention map, which allows integrating multiple stroke patterns into different spatial regions of the output image harmoniously. We demonstrate the effectiveness of our method, as well as generate comparable stylized images with multiple stroke patterns against the state-of-theart methods.

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Section: Introductionmentioning

confidence: 99%

Attention-Aware Multi-Stroke Style Transfer

Yao

Ren

Xie

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

165

126

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“…At the end of the last century there was a renaissance of textonbased approaches, including Zhu et al [248,249,270,271,269,272] on the mathematical modelling of textures and textons. A notable stride was the Bag of Textons (BoT) [114] and later Bag of Words (BoW) [42,216,238] approaches, where a dictionary of textons is generated, and images are represented statistically as orderless histograms over the texton dictionary.…”

mentioning

confidence: 99%

From BoW to CNN: Two Decades of Texture Representation for Texture Classification

et al. 2018

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Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention over several decades. Since 2000, texture representations based on Bag of Words (BoW) and on Convolutional Neural Networks (CNNs) have been extensively studied with impressive performance. Given this period of remarkable evolution, this paper aims to present a comprehensive survey of advances in texture representation over the last two decades. More than 250 major publications are cited in this survey covering different aspects of the research, including benchmark datasets and state of the art results. In retrospect of what has been achieved so far, the survey discusses open challenges and directions for future research.

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“…We start by reviewing the concept of the stroke size. Consider an image in style transfer as a composition of a series of small stroke textons, which are referred as the fundamental geometric micro-structures in images [20,41]. The stroke size of an image can be defined as the average scale of the composed stroke textons.…”

Section: Related Workmentioning

confidence: 99%

Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields

Jing

Liu

Yang

et al. 2018

Lecture Notes in Computer Science

101

112

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The Fast Style Transfer methods have been recently proposed to transfer a photograph to an artistic style in real-time. This task involves controlling the stroke size in the stylized results, which remains an open challenge. In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control. By analyzing the factors that influence the stroke size, we propose to explicitly account for the receptive field and the style image scales. We propose a StrokePyramid module to endow the network with adaptive receptive fields, and two training strategies to achieve faster convergence and augment new stroke sizes upon a trained model respectively. By combining the proposed runtime control strategies, our network can achieve continuous changes in stroke sizes and produce distinct stroke sizes in different spatial regions within the same output image.

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What Are Textons?

Cited by 35 publications

References 11 publications

Attention-Aware Multi-Stroke Style Transfer

Attention-Aware Multi-Stroke Style Transfer

From BoW to CNN: Two Decades of Texture Representation for Texture Classification

Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields

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