Semantic Context Matters: Analysis of Color Names Across Domains
- URL: http://arxiv.org/abs/2607.17221v1
- Date: Sun, 19 Jul 2026 12:24:18 GMT
- Title: Semantic Context Matters: Analysis of Color Names Across Domains
- Abstract summary: This paper investigates context-dependent color naming by mapping color-name datasets from Cosmetics, Crayola, and Car-color vocabularies onto the 86 fuzzy color categories of the COLIBRI color model.<n>The proposed framework supports the development of context-aware color models for design analytics, product search, recommendation systems, and human-centered artificial intelligence.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Color naming is influenced not only by physical color values but also by the semantic context in which colors are used. This paper investigates context-dependent color naming by mapping color-name datasets from Cosmetics, Crayola, and Car-color vocabularies onto the 86 fuzzy color categories of the COLIBRI color model. Contextual variation is analyzed using category coverage, Shannon entropy, and maximum lift. The results show that the three contexts occupy the COLIBRI color space differently: Cosmetics covers 48 of 86 fuzzy categories, Crayola covers 50, and Car colors cover 40. The results demonstrated that Crayola provides the broadest and most balanced use of the fuzzy color space, Cosmetics is mainly concentrated around warm-tone regions, and Car colors are more specialized around blue and achromatic regions. These findings show that color naming cannot be fully explained by numerical color similarity alone and that semantic context plays an important role in human color interpretation. The proposed framework supports the development of context-aware color models for design analytics, product search, recommendation systems, and human-centered artificial intelligence.
Related papers
- Not All Color Categories Are Equally Stable: A Multilingual Free Color Naming Experiment [0.0]
Green shades were consistently identified as green despite variations in appearance.<n>Yellow shades received a wider variety of names, including gold- and brown-related descriptions.<n>Red shades showed moderate naming consistency.
arXiv Detail & Related papers (2026-07-11T20:07:43Z) - Toward a Universal Color Naming System: A Clustering-Based Approach using Multisource Data [0.0]
Lack of universally accepted color naming standards leads to inconsistent color standards across platforms, applications, and industries.<n>We propose a clustering-based multisource data framework to build a standardized color-naming system.<n>This approach opens new opportunities for standardized, perceptually grounded color labeling in practical applications such as generative AI, visual search, and design systems.
arXiv Detail & Related papers (2026-01-30T21:25:00Z) - Color Names in Vision-Language Models [48.847573209643265]
We present the first systematic evaluation of color naming capabilities across vision-language models (VLMs)<n>Our results show that while VLMs achieve high accuracy on colors from classical studies, performance drops significantly on expanded, non-prototypical color sets.<n>We identify 21 common color terms that consistently emerge across all models, revealing two distinct approaches.
arXiv Detail & Related papers (2025-09-26T16:04:18Z) - Paint Bucket Colorization Using Anime Character Color Design Sheets [72.66788521378864]
We introduce inclusion matching, which allows the network to understand the relationships between segments.
Our network's training pipeline significantly improves performance in both colorization and consecutive frame colorization.
To support our network's training, we have developed a unique dataset named PaintBucket-Character.
arXiv Detail & Related papers (2024-10-25T09:33:27Z) - Control Color: Multimodal Diffusion-based Interactive Image Colorization [81.68817300796644]
Control Color (Ctrl Color) is a multi-modal colorization method that leverages the pre-trained Stable Diffusion (SD) model.
We present an effective way to encode user strokes to enable precise local color manipulation.
We also introduce a novel module based on self-attention and a content-guided deformable autoencoder to address the long-standing issues of color overflow and inaccurate coloring.
arXiv Detail & Related papers (2024-02-16T17:51:13Z) - L-CAD: Language-based Colorization with Any-level Descriptions using
Diffusion Priors [62.80068955192816]
We propose a unified model to perform language-based colorization with any-level descriptions.
We leverage the pretrained cross-modality generative model for its robust language understanding and rich color priors.
With the proposed novel sampling strategy, our model achieves instance-aware colorization in diverse and complex scenarios.
arXiv Detail & Related papers (2023-05-24T14:57:42Z) - ABANICCO: A New Color Space for Multi-Label Pixel Classification and
Color Segmentation [1.7205106391379026]
We propose a novel method combining geometric analysis of color theory, fuzzy color spaces, and multi-label systems for the automatic classification of pixels according to 12 standard color categories.
We present a robust, unsupervised, unbiased strategy for color naming based on statistics and color theory.
arXiv Detail & Related papers (2022-11-15T19:26:51Z) - PalGAN: Image Colorization with Palette Generative Adversarial Networks [51.59276436217957]
We propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention.
PalGAN outperforms state-of-the-arts in quantitative evaluation and visual comparison, delivering notable diverse, contrastive, and edge-preserving appearances.
arXiv Detail & Related papers (2022-10-20T12:28:31Z) - Can Language Models Encode Perceptual Structure Without Grounding? A
Case Study in Color [18.573415435334105]
We employ a dataset of monolexemic color terms and color chips represented in CIELAB, a color space with a perceptually meaningful distance metric.
Using two methods of evaluating the structural alignment of colors in this space with text-derived color term representations, we find significant correspondence.
We find that warmer colors are, on average, better aligned to the perceptual color space than cooler ones.
arXiv Detail & Related papers (2021-09-13T17:09:40Z) - Semantic-driven Colorization [78.88814849391352]
Recent colorization works implicitly predict the semantic information while learning to colorize black-and-white images.
In this study, we simulate that human-like action to let our network first learn to understand the photo, then colorize it.
arXiv Detail & Related papers (2020-06-13T08:13:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.