Calculative modeling for quantified semantics‐color mapping

Hu, Guoqing; Liu, Zheng; Wang, Yun; Sheng, Ai

doi:10.1002/col.22474

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…According to the results, the following conclusions were reached. There are 55 kinds of color matching schemes that can be grouped into four categories of color matching, using semantic analysis [46]. The four types of adjectives are fashion, technology, calm, and dazzling.…”

Section: Discussionmentioning

confidence: 99%

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The visual design of urban multimedia portals

Wang

Zhang²

2023

PLoS ONE

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In the visual design of a portal website, color is the first intuitive factor for users. It is relatively difficult for the designer of a city portal website to choose a color system that represents a city’s unique color from among the many available options. Therefore, this study extracted a decision-making model of the urban color system, which can help decision-makers and designers choose among color systems, and then effectively design a portal website that conforms to local cultural attributes. The proposed method to solve the problem involved obtaining optimal color matching by performing weight analysis of colors through 123 sample color semantics, factor analysis, and a fuzzy analytic hierarchy process. Semantic analysis was used to classify colors into four categories of fashion, technology, calm, and dazzling. The fashion color matching scheme scored relatively high. Web page color matching schemes with a white background were popular, among which a white and green color matching scheme scored relatively high. At the same time, there are differences in color preferences between genders and cultures. This study is significant because it proposes a color decision model for portal websites, which provides a reference value that can also be applied to the selection of color schemes for other types of web pages in the future.

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Section: Discussionmentioning

confidence: 99%

“…dazzling through the color semantic method [46]. The fashion category accounted for 30 categories; the technology category accounted for nine; the calm category accounted for ten; the dazzling category accounted for five.…”

Section: Plos Onementioning

confidence: 99%

The visual design of urban multimedia portals

Wang

Zhang²

2023

PLoS ONE

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“…With the rapid development of technology and the Internet, the accumulation of data will become unpredictable, and the visualization and analysis of the data after mining have become especially important. The potential correlations between data and the implied evolutionary laws can be seen [ 10 ]. Data visualization includes knowledge visualization, information visualization, scientific visualization, information graphics, and visual design.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network-Based Cross-Media Semantic Matching and User Adaptive Satisfaction Analysis Model

Jiang¹

2022

Computational Intelligence and Neuroscience

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In this paper, an in-depth study of cross-media semantic matching and user adaptive satisfaction analysis model is carried out based on the convolutional neural network. Based on the existing convolutional neural network, this paper uses rich information. The spatial correlation of cross-media semantic matching further improves the classification accuracy of hyperspectral images and reduces the classification time under user adaptive satisfaction complexity. Aiming at the problem that it is difficult for the current hyperspectral image classification method based on convolutional neural network to capture the spatial pose characteristics of objects, the problem is that principal component analysis ignores some vital information when retaining a few components. This paper proposes a polymorphism based on extension Attribute Profile Feature (EMAP) Stereo Capsule Network Model for Hyperspectral Image Classification. To ensure the model has good generalization performance, a new remote sensing image Pan sharpening algorithm based on convolutional neural network is proposed, which increases the model’s width to extract the feature information of the image and uses dilated instead of traditional convolution. The experimental results show that the algorithm has good generalization while ensuring self-adaptive satisfaction.

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“…Intelligent algorithms mostly focus on the design rate of color matching of products [8][9][10] and the optimization of affective relevance models. 11,12 But, ignore the problem of emotional solutions and combination strategies of multicolor combinations under the goal of determining emotional intentions. In addition, it in turn weakens the diversity and innovation of color combinations.…”

Section: Introductionmentioning

confidence: 99%

“…A combination color emotion is different from a single color emotion, as there is a superposition of emotional properties, integration, conflict, and other complex relationships. Intelligent algorithms mostly focus on the design rate of color matching of products 8–10 and the optimization of affective relevance models 11,12 . But, ignore the problem of emotional solutions and combination strategies of multicolor combinations under the goal of determining emotional intentions.…”

Section: Introductionmentioning

confidence: 99%

Emocolor: An assistant design method for emotional color matching based on semantics and images

Zhang

Wang

et al. 2023

Color Research & Application

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Much research has been done on the relationship between emotions and colors, and many color scheme recommendation tools have been developed. These tools can often help recommend a suitable color scheme to express a certain emotion, but it is still difficult to design satisfying schemes based on such recommendations. In this study, based on the Color Scheme Bible, Compact Edition, we developed Emocolor, which allows professionals to generate a large number of color schemes based on emotional words or emotional images. It is also used to iteratively optimize the generated color schemes based on an interactive genetic algorithm to find the color scheme design that best matches the user's emotion. Through the evaluation of tram color schemes based on emotional words, Emocolor can effectively generate tram color schemes that match users' emotions. It can also effectively transfer the emotion of an image with a single dominant color to a tram color scheme. In short, Emocolor can help professionals describe the relationship between emotion and color in a more accurate way and design emotional color schemes. Emocolor can be applied in advertising design, product design, interior design, and other fields.

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Calculative modeling for quantified semantics‐color mapping

Cited by 8 publications

References 19 publications

The visual design of urban multimedia portals

The visual design of urban multimedia portals

Convolutional Neural Network-Based Cross-Media Semantic Matching and User Adaptive Satisfaction Analysis Model

Emocolor: An assistant design method for emotional color matching based on semantics and images

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