XCI-Sketch - Extraction of Color Information from Images for Generation of Colored Outlines and Sketches

Aug 23, 2026

Sketches communicate visual scenes through an individual’s creative viewpoint, but grayscale rendering often limits expressivity and information conveyance. XCI-Sketch introduces two methods to emulate human-colored sketches using the Contour Drawing Dataset. One pipeline transfers color to produce colored outline sketches via image processing with k-means clustering, while the other employs a generative adversarial network to synthesize colored sketches from unseen images, validated using quantitative and qualitative evaluations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Related Works

Approaches for rendering sketches

GANs for generating sketches

Methodology

Colored outline rendering

Color space manipulation

GAN-based colored sketch generation

Experiments and Evaluation

Quantitative and qualitative assessment

Perceptual user study

Conclusion

XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches V Manushree 􀀃 Sameer Saxena􀀃 Parna Chowdhury􀀃 Manisimha Varma􀀃 Harsh Rathod􀀃 Ankita Ghosh Sahil Khose Manipal Institute of Technology, Manipal { manushree .v, sameer .saxena, parna .chowdhury, manthena .varma, harsh.rathod, ankita.ghosh1, [sahil.khose }@learner.manipal.edu](sahil.khose }@learner.manipal.edu) Abstract Sketches are a medium to convey a visual scene from an individual's creative perspective. The addition of color substantially enhances the overall expressivity of a sketch. This paper proposes two methods to mimic human-drawn colored sketches by utilizing the Contour Drawing Dataset. Our ﬁrst approach renders colored outline sketches by applying image processing techniques aided by k-means color clustering. The second method uses a generative adversarial network to develop a model that can generate colored sketches from previously unobserved images. We assess the results obtained through quantitative and qualitative evaluations. 1 Introduction Sketches are elementary representations of visual scenes. They are easier to draw and are not limited to artists, making them an accessible and common form of art. Sketches ﬁnd application in various industries pertaining to advertisement, fashion designing, architecture, and interior designing, where illustrations play a signiﬁcant role. Often while using grayscale sketches, the overall expressivity falls short, and it is challenging to derive the necessary information. Color, being an integral part of art, appeals to the human senses. It elevates the sketch and efﬁciently carries across its meaning, background details, and connotation. In recent years, image processing and deep learning techniques have been extensively deployed to synthesize artwork of various forms. Previously, attempts have been made to leverage images to generate sketches, but these sketches have been limited to black and white shades. Our paper focuses on different approaches to convert photographic pictures into colored sketches. We use the Contour Drawing Dataset presented in the work by Li [20] and propose two ways to extract color information from the images and amalgamate it with the corresponding sketches. Our three-fold contribution which is explained in detail in the succeeding sections of the paper is as follows: ﬁrst, we formulate a process to transfer color onto the existing black and white sketches in the dataset to produce colored outlines. Second, we propose a method to produce colored sketches by performing colorspace manipulation. Finally, we go a step further to use these sketches as the training dataset for a generative adversarial network and develop a model which can produce colored sketches from unseen images. We discuss the related literature based on which we have modeled our approaches in Section 2 . The algorithm for rendering colored outlines is explained in Section 3 . Section 4 describes the image 􀀃 Authors have contributed equally to this work and share ﬁrst authorship. Link of the code: [https://github.com/Sampai28/GeneratedSketches](https://github.com/Sampai28/GeneratedSketches) 1st CtrlGen: Controllable Generative Modeling in Language and Vision Workshop at NeurIPS 2021 . processing techniques employed to produce color sketches, followed by the methodology used for generating colored sketches using a generative model. Section 5 evaluates our results by comparing the produced sketches with existing sketch datasets and conducting a perceptual user study. We conclude by suggesting various possible applications of our work in Section 6 and the scope of improvement in Section 7 . 2 Related Works Approaches for rendering sketches: A wide range of research work has been done by the computer vision and computer graphics community in the domain of sketch synthesis. Prior techniques include rendering sketches by applying edge detection [3, 19], co

FAQs

XCI-Sketch的核心目标是什么？

该工作旨在从图像中提取颜色信息，并生成带颜色的描边草图与彩色草图。

文中提出了哪些生成彩色草图的方法？

作者提出两种方式：一种结合图像处理与k-means聚类生成彩色描边草图；另一种使用生成对抗网络（GAN）从未见图像生成彩色草图。

实验如何评估生成结果？

评估包含定量与定性对比，并结合与现有草图数据集的比较以及感知用户研究来衡量效果。

Aug 23, 2026

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