Color Theme Research Articles

Fabric image recolorization based on intrinsic image decomposition

Fabric image recolorization is widely used in assisting designers to generate new color design proposals for fabric. In this paper, a new image recolorization method is proposed. Different from classical image recolorization methods, which need some complicated interactive operations from users, our proposed method can achieve automatic recolorization of images. The proposed method contains three sequential phases: a phase of extracting representative colors from fabric images; an image segmentation phase; and an image reconstruction phase by using given color themes. Integrated with intrinsic image decomposition, a new image segmentation model is designed in a variational framework, and an algorithm is given to solve the model. Our image recolorization results are images that are reconstructed by the composition of the image segmentation results and the given color themes. Numerical results demonstrate that our newly proposed intrinsic image decomposition-based image recolorization method can generate better results than the classical cartoon-and-texture decomposition-based method.

Mediating Color Filter Exploration with Color Theme Semantics Derived from Social Curation Data

Despite the popularity of photo editors used to improve image attractiveness and expressiveness on social media, many users have trouble making sense of color filter effects and locating a preferred filter among a set of designer-crafted candidates. The problem gets worse when more computer-generated filters are introduced. To enhance filter findability, we semantically name and organize color effects leveraging data curated by creative communities online. We first model semantic mappings between color themes and keywords in everyday language. Next, we index and organize each filter by the derived semantic information. We conduct three separate studies to investigate the benefit of the semantic features on filter exploration. Our results indicate that color theme semantics constructed through social curation enhances filter findability, providing important implications into how to use the wisdom of the crowd to improve user experience with image editors.

3D color homography model for photo-realistic color transfer re-coding

Color transfer is an image editing process that naturally transfers the color theme of a source image to a target image. In this paper, we propose a 3D color homography model which approximates photo-realistic color transfer algorithm as a combination of a 3D perspective transform and a mean intensity mapping. A key advantage of our approach is that the re-coded color transfer algorithm is simple and accurate. Our evaluation demonstrates that our 3D color homography model delivers leading color transfer re-coding performance. In addition, we also show that our 3D color homography model can be applied to color transfer artifact fixing, complex color transfer acceleration, and color-robust image stitching.

Group‐Theme Recoloring for Multi‐Image Color Consistency

AbstractModifying the colors of an image is a fundamental editing task with a wide range of methods available. Manipulating multiple images to share similar colors is more challenging, with limited tools available. Methods such as color transfer are effective in making an image share similar colors with a target image; however, color transfer is not suitable for modifying multiple images. Approaches for color consistency for photo collections give good results when the photo collection contains similar scene content, but are not applicable for general input images. To address these gaps, we propose an application framework for achieving color consistency for multi‐image input. Our framework derives a group color theme from the input images′ individual color palettes and uses this group color theme to recolor the image collection. This group‐theme recoloring provides an effective way to ensure color consistency among multiple images and naturally lends itself to the inclusion of an additional external color theme. We detail our group‐theme recoloring approach and demonstrate its effectiveness on a number of examples.

A Data-Driven Approach for Furniture and Indoor Scene Colorization.

We present a data-driven approach that colorizes 3D furniture models and indoor scenes by leveraging indoor images on the internet. Our approach is able to colorize the furniture automatically according to an example image. The core is to learn image-guided mesh segmentation to segment the model into different parts according to the image object. Given an indoor scene, the system supports colorization-by-example, and has the ability to recommend the colorization scheme that is consistent with a user-desired color theme. The latter is realized by formulating the problem as a Markov random field model that imposes user input as an additional constraint. Our system is able to imitate the colorization results for those scenes containing the same type of objects, but with spatially varied patterns. We contribute to the community a hierarchically organized image-model database with correspondences between each image and the corresponding model at the part-level. Our experiments and a user study show that our system produces perceptually convincing results comparable to those generated by interior designers.

Finding intrinsic color themes in images with human visual perception

Extracting color themes from an image is to get a color palette that consists of dominant colors of the image. In this article, we construct a color network to build the intrinsic connections of color information of pixels. By applying improved linear iterative clustering (SLIC) algorithm [1,2], we obtain initial color themes. In the following stage, with learning from human-extract color themes, we can get the final sorted color themes result. Experimental results demonstrate that our model outperforms previous approaches in terms of the number of themes, span and accuracy.

Color Orchestra: Ordering Color Palettes for Interpolation and Prediction.

Color theme or color palette can deeply influence the quality and the feeling of a photograph or a graphical design. Although color palettes may come from different sources such as online crowd-sourcing, photographs and graphical designs, in this paper, we consider color palettes extracted from fine art collections, which we believe to be an abundant source of stylistic and unique color themes. We aim to capture color styles embedded in these collections by means of statistical models and to build practical applications upon these models. As artists often use their personal color themes in their paintings, making these palettes appear frequently in the dataset, we employed density estimation to capture the characteristics of palette data. Via density estimation, we carried out various predictions and interpolations on palettes, which led to promising applications such as photo-style exploration, real-time color suggestion, and enriched photo recolorization. It was, however, challenging to apply density estimation to palette data as palettes often come as unordered sets of colors, which make it difficult to use conventional metrics on them. To this end, we developed a divide-and-conquer sorting algorithm to rearrange the colors in the palettes in a coherent order, which allows meaningful interpolation between color palettes. To confirm the performance of our model, we also conducted quantitative experiments on datasets of digitized paintings collected from the Internet and received favorable results.

Screenshot-based color compatibility assessment and transfer for Web pages

Colors play particularly important roles in both designing and accessing Web pages. A well-designed color scheme improves the visual aesthetic of Web pages and facilitates user interactions. As far as we know, existing studies on color compatibility assessment and enhancement focus on images, and the assessment and enhancement for Web colors are rare. In order to aid Web designers evaluate and choose colors, this paper investigates color compatibility assessment for Web pages and applies this assessment to Web color editing based on Web screenshots rather than source codes. This study consists of four parts. First, the roles of color design in Web pages are discussed and a screenshot-based approach is proposed for the analysis of Web page regions. Second, a new method for extracting the color theme of a Web page is proposed. Then, we construct an assessment model that attributes scores to the color compatibility of Web pages through transfer learning. Third, we examine Web color transfer and combine it with the learned compatibility assessment model to create a new application that recommends colors for Web design. Lastly, the evaluation results suggest that the constructed compatibility assessment model and the proposed color transfer technique are effective and are superior over conventional methods. User studies suggest that our color recommendation application can generate new Web page screenshots that have higher color compatibility scores in comparison with those of the original pages.

Cartoon and Texture Decomposition-Based Color Transfer for Fabric Images

A color design process for fabric images can resort to a solution of a color transfer problem based on given color themes. Usually, the color transfer process contains an image segmentation phase and an image construction phase. In this paper, a novel color transfer method for fabric images is proposed. Compared with classical color transfer methods, the new method has the following three main innovations. First, the new method, in its image segmentation phase, follows an assumption that a fabric image can be decomposed into cartoon and texture components, which means the new color transfer method, in its image segmentation, phase incorporates an image decomposition process. The advantage of the innovation is that the cartoon component is more suitable than the original image to be used to partition the fabric image. Second, the new color transfer method can generate more vivid color transfer results since the above texture component is used to describe yarn texture details in the image construction phase. Third, the total generalized variation (TGV) regularizer is used to further improve the performance of image decomposition. Here, the TGV regularizer is good at estimating the weak lightness variation of the cartoon component with the CIELab color scheme. In addition, by using the augmented Lagrange multiplier method, we derive an efficient algorithm to search for the solutions to the proposed color transfer problem. Numerical results demonstrate that the proposed color transfer method can generate better results for fabric images.

Attention-aware color theme extraction for fabric images

Color configuration plays an important role in art, design, and communication, which can influence the user’s experiences, feelings, and psychological well-being. It is laborious to manually select a color theme from scratch for handling large batches of images. Alternatively, it can inspire designers’ creations and save their time as well by leveraging the color themes in existing art works (e.g. fabric, paintings). However, it is challenging to automatically extract perceptually plausible color themes from fabric images. This paper presents a new automatic framework for extracting color themes from fabric images. A saliency map is built to help recognize the visual attention regions of the input image. Since the saliency map separates the image into visual attention regions (foreground) and non-visual attention regions (background), we respectively compute the dominant colors of these two regions, and merge them to form the initial target color theme based on certain rules. The dominant colors are extracted, accounting for the characteristics of the fabric and hue distributions of fabric images so as to acquire visually plausible results. Our method can be used to transfer colors between two fabric images for fabric color design. We tested our method thoroughly with various fabric images (e.g. cotton, silk, and linen) with different texture patterns (e.g. plain and twill). Experiments show that our method is more efficient and can generate more visually plausible results than state-of-the-art algorithms.

Artistic Coloring: Color Transfer from Painting

Color transfer is to alter an image’s color composition by reference to the color characteristics of another image. In this paper, we build a system called artistic coloring that realizes automatic color transfer from famous paintings. It properly extracts the wonderful color characteristics of famous paintings and applies them to color transfer. Specially, we investigate the traditional color theme extraction methods and find their deficiencies. Based on this, we quantify the processing of human painting to find the main colors in the color palette and propose an artistic balanced color theme extraction algorithm aimed specially at paintings. In the experiments, a user study is carried out to evaluate the artistic balanced extraction method. Our proposed method achieves the highest score 3.98 in a 5-point evaluation, which is 41% higher than traditional methods at most. We have successfully tested the artistic coloring system on lots of images with different painting styles. The results are natural and have the similar color characteristics with their corresponding reference paintings.

5. Bessarabian in the Modern Engraving Constitution and Interference with its European Art

Abstract Runing the new century (XX) attests in Bessarabian art, the experience of several decades of professional artistic activity (the first Evening School of Drawing appears in Chisinau in 1887 and is due scholar Academy of Arts in St. Petersburg, Terinte Zubcu). Such short experience has not met any of the neighboring countries' national schools. In Bessarabia from the beginning of the twentieth century is established the main areas of professional art – painting- with remarkable portraits, landscapes, genre paintings; sculpture with respective genres; stampa as a kind of graphics. Marked by the period and by the influences of art schools, where Bessarabians have studied, it is clear that in painting and sculpture and graphics in the first round, have dominated peredvizhnik influences their color and monochrome theme. Guidelines of Bessarabian plastic artists in the development phase of modern art, is the decisive moment of establishment of the Bessarabian engraving as a kind of professional art, marked by tendencies that have appeared in European art and Russian at the limit of nineteenth and twentieth centuries, such as Expressionism in the works of Sneer Cogan, George Ceglocof and Art 1900 in the works of Theodor Kiriacoff, Elisabeth Ivanovsky or Moissey Kogan.

Parking Sensing and Information System: Sensors, Deployment, and Evaluation

This paper describes a smart parking sensing and information system that disseminates parking availability information to public users in a cost-effective and efficient manner. The hardware framework of the system is built on advanced wireless sensor networks and cloud service over the Internet, and the system is highly scalable. The parking information provided to the users is set in the form of occupancy rates and expected cruising time. Both are obtained from an analytical algorithm that processes historical and real-time data and are then visualized in a color theme. The entire parking system is deployed and extensively evaluated at Stanford University, California, Parking Structure 1.

Eco-design and color, a proposal for innovation and methodological approach

En la actualidad, la metodología del ecodiseño aplicada al diseño de objetos presenta un gran auge, con numerosas marcas y firmas que convierten a la reducción de los impactos medioambientales en las etapas de diseño como un valor agregado al desarrollo de sus productos. Sin embargo, en cuanto al uso y aplicación del color en estas nuevas líneas de objetos es muy poco lo que se ha analizado o propuesto e incluso abordado desde el ámbito académico. Es el diseñador quien conceptualiza y otorga cualidades a un objeto, como lo es entre otras el color. En el marco del nuevo paradigma de la sustentabilidad y el trabajo de diseño pareciera que el tema del color solamente se asocia a escalas cromáticas que nos conducen a los colores neutros o a un imaginario social que se ubicaría dentro del verde. A partir del proyecto de investigación “Innovación y desarrollo de productos para el ITSPV con base en artículos de desuso” se abre un nuevo análisis y enfoque metodológico que pone al color como parte del proceso de diseño, dentro de las opciones a escoger e investigar por los diseñadores, respetando la naturaleza del planteo del ecodiseño, pero pensando cromáticamente los objetos. En el presente artículo se exhiben algunos productos y trabajos en donde el color y el ecodiseño se acercan para generar una nueva metodología de trabajo basada en la creatividad, la cromaticidad, la identidad cultural y la disminución del impacto medio ambiental en los procesos de diseño.

Hierarchical emotional color theme extraction

AbstractArtists usually carefully select different colors in artistic work so as to convey special visual and emotional feelings. Color theme extraction techniques can help users to acquire the color styles in an image. However, current color theme extraction methods ignore the emotional factors, and they can only provide a single theme result for an image as well, neither of which meet people's favor on different colors under different mood states. This article introduces the conception of emotional color theme, introduces the color emotion theory into color theme extraction, and proposes a novel emotion color theme extraction framework. To achieve these goals, we perform the theme extraction with emotion value of each pixel instead of color value. The emotional discrepancy is proposed between the colors in the theme to evaluate a color theme quality and prove it with color theme dataset. Then a data driven approach is adopted to optimize the color theme. Different from previous optimizing strategies, we built the emotional relation between the target theme and the candidate theme. Our method can enhance the color editing results of previous methods, e.g., for color transfer our results can demonstrate hierarchical emotions from a single reference image. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 513–522, 2016

Em busca do "tipo eugênico nacional": Uma discussão sobre cor e raça nas caricaturas da revista "Careta" (1930-1934)

The purpose of this paper is to present the results of our study on color and race in the Interim Government having as source the cartoons magazine Careta. In that sense, we seek to realize through the illustrations to the journal's position in the racial question and guide the eugenic discourse wrapped in the color theme and race in the period. In other words, how eugenics appeared in the speech of some doctors and intellectuals and the periodic was positioned in relation to the so-called crossing race. Among the various possibilities of working the issue of eugenics, the guided sources in cartoons proved as rich material to perceive race and skin color related to the discourse over nationality.

Enhancing harmony of color theme by changing the order of colors

Enhancing harmony of color theme by changing the order of colors

Affective Image Colorization

Colorization of gray-scale images has attracted many attentions for a long time. An important role of image color is the conveyer of emotions (through color themes). The colorization with an undesired color theme is less useful, even it is semantically correct. However this has been rarely considered. Automatic colorization respecting both the semantics and the emotions is undoubtedly a challenge. In this paper, we propose a complete system for affective image colorization. We only need the user to assist object segmentation along with text labels and an affective word. First, the text labels along with other object characters are jointly used to filter the internet images to give each object a set of semantically correct reference images. Second, we select a set of color themes according to the affective word based on art theories. With these themes, a generic algorithm is used to select the best reference for each object, balancing various requirements. Finally, we propose a hybrid texture synthesis approach for colorization. To the best of our knowledge, it is the first system which is able to efficiently colorize a gray-scale image semantically by an emotionally controllable fashion. Our experiments show the effectiveness of our system, especially the benefit compared with the previous Markov random field (MRF) based method.

Affective image adjustment with a single word

We present a complete system that automatically adjusts image color to meet a desired emotion. It will be more convenient for users, especially for non-professional users, to adjust an image with a semantic user input, for example, to make it lovelier. The whole algorithm is fully automatic, without any user interactions, and the inputs are simply the original image and an affective word (e.g. lovely). To achieve this goal, we solve several non-trivial problems. First, in order to find the proper color themes (template of colors) to reflect the expression of the affective word, we exploit the theoretical and empirical concepts in famous art theories and build a color theme--affective word relation model allowing efficient selection of candidate themes. Furthermore, we propose a novel strategy to select the most suitable color theme among the candidates. Second, to adjust image colors, we propose the Radial Basis Functions (RBF) based interpolation method, which is more effective in many scenarios as evidenced in experiments. We also evaluate the system with comprehensive user studies and its capability is confirmed by the results.

Fuzzy region competition-based auto-color-theme design for textile images

We propose a model to recolor textile images by different color themes. The model contains three phases. The first phase is to partition an input textile image into several homogeneous regions. The CIELab color mean of each region and a bias-field function are obtained from the segmentation results. The combination of the color mean values of all regions is considered as the color theme of the input image. The second phase is to retrieve the relevant color themes from a given dataset. The retrieved color themes preserve the color mood of the input image in the sense of the similarity measurement defined in the color mood space. In the third phase, we reconstruct new images with different appearances from the input image by using the retrieved color themes. The proposed method provides a powerful tool for designers to generate and search for all relevant color combinations related to a given theme. Numerical results indicate that our recolorization model performs well on complex textile design patterns.