{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:33:21Z","timestamp":1761006801814,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:00:00Z","timestamp":1760745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>The segmentation and classification of color are crucial stages in image processing, computer vision, and pattern recognition, as they significantly impact the results. The diverse, hand-labeled datasets in the literature are applied for monochromatic or color segmentation in specific domains. On the other hand, synthetic datasets are generated using statistics, artificial intelligence algorithms, or generative artificial intelligence (AI). This last one includes Large Language Models (LLMs), Generative Adversarial Neural Networks (GANs), and Variational Autoencoders (VAEs), among others. In this work, we propose VitralColor-12, a synthetic dataset for color classification and segmentation, comprising twelve colors: black, blue, brown, cyan, gray, green, orange, pink, purple, red, white, and yellow. VitralColor-12 addresses the limitations of color segmentation and classification datasets by leveraging the capabilities of LLMs, including adaptability, variability, copyright-free content, and lower-cost data\u2014properties that are desirable in image datasets. VitralColor-12 includes pixel-level classification and segmentation maps. This makes the dataset broadly applicable and highly variable for a range of computer vision applications. VitralColor-12 utilizes GPT-5 and DALL\u00b7E 3 for generating stained-glass images. These images simplify the annotation process, since stained-glass images have isolated colors with distinct boundaries within the steel structure, which provide easy regions to label with a single color per region. Once we obtain the images, we use at least one hand-labeled centroid per color to automatically cluster all pixels based on Euclidean distance and morphological operations, including erosion and dilation. This process enables us to automatically label a classification dataset and generate segmentation maps. Our dataset comprises 910 images, organized into 70 generated images and 12 pixel segmentation maps\u2014one for each color\u2014which include 9,509,524 labeled pixels, 1,794,758 of which are unique. These annotated pixels are represented by RGB, HSL, CIELAB, and YCbCr values, enabling a detailed color analysis. Moreover, VitralColor-12 offers features that address gaps in public resources such as violin diagrams with the frequency of colors across images, histograms of channels per color, 3D color maps, descriptive statistics, and standardized metrics, such as \u0394E76, \u0394E94, and CIELAB Chromacity, which prove the distribution, applicability, and realistic perceptual structures, including warm, neutral, and cold colors, as well as the high contrast between black and white colors, offering meaningful perceptual clusters, reinforcing its utility for color segmentation and classification.<\/jats:p>","DOI":"10.3390\/data10100165","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T09:23:34Z","timestamp":1760952214000},"page":"165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VitralColor-12: A Synthetic Twelve-Color Segmentation Dataset from GPT-Generated Stained-Glass Images"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3897-6212","authenticated-orcid":false,"given":"Mart\u00edn","family":"Montes Rivera","sequence":"first","affiliation":[{"name":"Unidad Acad\u00e9mica de Ciencia y Tecnolog\u00eda de la Luz y la Materia, Universidad Aut\u00f3noma de Zacatecas, Campus es Parque de Ciencia y Tecnolog\u00eda QUANTUM, Cto., Zacatecas 98160, Mexico"},{"name":"Research and Postgraduate Studies, Department of Universidad Polit\u00e9cnica de Aguascalientes, Aguascalientes 20342, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0721-8515","authenticated-orcid":false,"given":"Carlos","family":"Guerrero-Mendez","sequence":"additional","affiliation":[{"name":"Unidad Acad\u00e9mica de Ciencia y Tecnolog\u00eda de la Luz y la Materia, Universidad Aut\u00f3noma de Zacatecas, Campus es Parque de Ciencia y Tecnolog\u00eda QUANTUM, Cto., Zacatecas 98160, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0710-6062","authenticated-orcid":false,"given":"Daniela","family":"Lopez-Betancur","sequence":"additional","affiliation":[{"name":"Unidad Acad\u00e9mica de Ciencia y Tecnolog\u00eda de la Luz y la Materia, Universidad Aut\u00f3noma de Zacatecas, Campus es Parque de Ciencia y Tecnolog\u00eda QUANTUM, Cto., Zacatecas 98160, Mexico"},{"name":"Unidad Acad\u00e9mica de Ingenier\u00eda l, Universidad Aut\u00f3noma de Zacatecas, Zacatecas 98000, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tonatiuh","family":"Saucedo-Anaya","sequence":"additional","affiliation":[{"name":"Unidad Acad\u00e9mica de Ciencia y Tecnolog\u00eda de la Luz y la Materia, Universidad Aut\u00f3noma de Zacatecas, Campus es Parque de Ciencia y Tecnolog\u00eda QUANTUM, Cto., Zacatecas 98160, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0012-018X","authenticated-orcid":false,"given":"Manuel","family":"S\u00e1nchez-C\u00e1rdenas","sequence":"additional","affiliation":[{"name":"Research and Postgraduate Studies, Department of Universidad Polit\u00e9cnica de Aguascalientes, Aguascalientes 20342, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6654-3313","authenticated-orcid":false,"given":"Salvador","family":"G\u00f3mez-Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Unidad Acad\u00e9mica de Ingenier\u00eda l, Universidad Aut\u00f3noma de Zacatecas, Zacatecas 98000, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1016\/S0031-3203(00)00149-7","article-title":"Color Image Segmentation: Advances and Prospects","volume":"34","author":"Cheng","year":"2001","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Al Garea, S., and Das, S. 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