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Harmonious color combinations can stimulate positive user emotional responses. However, a widely open research question is: how can we establish a robust and accurate color harmony measure for the public and professional designers to identify the harmony level of a color theme or color set. Building upon the key discovery that color pairs play an i...
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... The result shows that the algorithm can reduce designers' work barriers and speed up design [16]. Yang et al. took color pairs as the research object, and based on the positive effect of color harmony on emotion, an initial prediction of color harmony was given, and then the initial score was corrected by Back Propagation Neural Network, so as to construct a more flexible color harmony model [17]. The above researches are carried out independently for a specific application, and have achieved good results in related fields, but the model has poor interpretability. ...
Color harmony is the hotspot of many researchers in the fields of art and design which is widely used in various artworks and design activities. As for psychology, color harmony is closely related to human’s perception and cognition, although, the research of its generation process has not been fully focused on. With experimental psychology with artificial intelligence technology combined together, our aim is to investigate the factors affecting color harmony on the basis of the generation process from perception to cognition, and construct a mathematical model to predict its degree, so as to realize the quantitatively analysis and measurement of color harmony. We classify the factors affecting color harmony into objective and subjective ones. Among them, the objective factors are constructed based on the three principle elements of color with a total of 21-dimensional color-pair objective physical features extracted. And the subjective factors are divided into direct psychological effects and indirect psychological effects. 180 two-color combinations were evaluated by 30 subjects based on color harmony and its subjective factors on discrete scales ranging from −2 to 2. Correlation analysis reveals that color harmony is not only affected by objective factors, but also by subjective factors which has a stronger correlation than that of the objective ones. Finally, machine learning algorithms were adopted to construct a mathematical model, which has been proved that the linear relationship can well explain the generation mechanism of color harmony.
... Color plays a critical role in images; even a limited set of colors can represent an entire image. This "set" of dominant colors is called a color palette and is central to various research tasks such as retrieving images using dominant colors [1]- [3] or color sketches [4], analyzing image color strategies [5], [6] or image color harmony [7], and transferring colors using dominant colors [8], [9]. Most of the abovementioned studies use the histogram or clustering-based methods [10]. ...
The dominant colors in an image can be used for image search, color editing, palette generation, and several other applications. Conventionally, dominant colors are extracted using clustering or histogram-based methods. However, these methods cannot extract the dominant colors of small regions, which are essential for the analysis of color schemes. This study developed an approach to automatically extract dominant colors based on color features that are typically considered by human observers when analyzing color schemes. The proposed method first calculates the initial dominant color candidates using the K-means algorithm in the CIELAB color space and the graph cut of a region adjacency graph (RAG) of the segmented image. Next, the algorithm calculates the color features such as the saturation, contrast, and area of each cluster, based on which it extracts the dominant colors. Our method can extract prominent colors from small image regions as the dominant colors, which is not possible using conventional methods.
... These methods first collect large numbers of color samples; then, they employ machine learning models that accept statistical variables for colors as the input features to predict the quality of a color scheme. Because these methods use supervised learning models, volunteers are asked to rate and label these samples in advance (Kita & Miyata, 2016;O'Donovan et al., 2011O'Donovan et al., , 2014Yang et al., 2019). For instance, Kita and Miyata (2016) developed a least absolute shrinkage and selection operator (LASSO) regressionbased learning model with color features, such as the mean lightness and saturation values, to evaluate color schemes. ...
... Previous studies have applied machine learning models in image processing to evaluate the color quality of images (Kita & Miyata, 2016;O'Donovan et al., 2011O'Donovan et al., , 2014Yang et al., 2019), but to the best of our knowledge, this is the first study to apply learning-based color quality evaluation to map visualization. Using our approach, the quality of sequential color schemes designed by mapmakers can be automatically evaluated. ...
Color quality evaluation is key to judging map quality, which can improve data visualization and communication. However, most existing methods for evaluating map colors are tedious and subjective manual methods. In this paper, we study sequential color schemes, a widely used map color type and propose a learning-based approach for evaluating the color quality. The approach consists of two steps. First, we extract and characterize the cartographic factors for determining the quality of sequential color schemes, such as color order, color match, color harmony, color discrimination and color uniformity. Second, we present a model to predict the color quality based on AdaBoost, a type of ensemble learning algorithm with excellent classification performance and use these factors as input data. We conduct a case study based on 781 samples and train the AdaBoost-based model to predict the quality of sequential color schemes. To evaluate the model’s performance, we calculated the area under the receiver operating characteristic (ROC) curve (AUC). The AUC values are 0.983 and 0.977 on the training data and testing data, respectively. These results indicate that the proposed approach can be used to automatically evaluate the quality of sequential color schemes for maps, which helps mapmakers select good colors.
Color composition (or color theme) is a key factor to determine how well a piece of art work or graphical design is perceived by humans. Despite a few color harmony models have been proposed, their results are often less satisfactory since they mostly neglect the variations of aesthetic cognition among individuals and treat the influence of all ratings equally as if they were all rated by the same anonymous user. To overcome this issue, in this paper we propose a new color theme evaluation model by combining a back propagation neural network and a kernel probabilistic model to infer both the color theme rating and the user aesthetic preference. Our experiment results show that our model can predict more accurate and personalized color theme ratings than state of the art methods. Our work is also the first-of-its-kind effort to quantitatively evaluate the correlation between user aesthetic preferences and color harmonies of five-color themes, and study such a relation for users with different aesthetic cognition.
Judging how an image is visually appealing is a complicated and subjective task. This highly motivates having a machine learning model to automatically evaluate image aesthetic by matching the aesthetics of general public. Although deep learning methods have been successfully learning good visual features from images, correctly assessing image aesthetic quality is still challenging for deep learning. To tackle this, we propose a novel multi-view convolutional neural network to assess image aesthetic by analyzing image color composition and space formation (IAACS). Specifically, from different views of an image, including its key color components with their contributions, the image space formation and the image itself, our network extracts their corresponding features through our proposed feature extraction module (FET) and the ImageNet weight-based classification model. By fusing the extracted features, our network produces an accurate prediction score distribution of image aesthetic. Experiment results have shown that we have achieved a superior performance.
The selection of text color is a time-consuming and important aspect in the designing of visual-textual presentation layout. In this paper, we propose a novel deep neural network architecture for predicting text color in the designing of visual-textual presentation layout. The proposed architecture consists of a text colorization network, a color harmony scoring network, and a text readability scoring network. The color harmony scoring network is learned by training with color theme data with aesthetic scores. The text readability scoring network is learned by training with design works. Finally, the text colorization network is designed to predict text colors by maximizing both color harmony and text readability, as well as learning from designer’s choice of color. In addition, this paper conducts a comparison with other methods based on random generation, color theory rules or similar features search. Both quantitative and qualitative evaluation results demonstrate that the proposed method has better performance.
