A Novel Color Difference-Based Method for Palette Extraction and Evaluation Using Images of Birds

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dc.authorid0000-0002-1944-1928
dc.contributor.authorKosesoy, Melike Bektas
dc.contributor.authorYilmaz, Seckin
dc.date.accessioned2026-02-08T15:15:41Z
dc.date.available2026-02-08T15:15:41Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractColor palettes are important sources of inspiration for designers. In most coloring studies, designers and interior architects use ready-made color palettes that are known to be harmonious and popular with clients. In this study, a new color palette extraction and evaluation method is proposed that differs from existing methods because it accelerates designers' product coloring processes by identifying the color-area and color-neighborhood relationship. Furthermore, attractive color palettes will be identified by focusing on living creatures, primarily colorful birds. For this purpose, a new data set of images of birds was created. In the literature, the success of color palettes is usually evaluated through questionnaires, while measurements are made without relying on numerical metrics. In this study and, for the first time, a new metric based on CIEDE2000 and the Hungarian algorithm to measure the accuracy of color palettes is demonstrated. In the proposed method birds are first segmented from background using Mask R-CNN and PointRend models. Then, Fuzzy c-means (FCM), Gaussian Mixture Model (GMM), k-means, Mean Shift, and mini batch k-means algorithms were used to extract the color palette of each bird image. Color reduction based on the CIEDE2000 was performed on the color palettes using various threshold values, resulting in the final color palettes. In the last step, color-area and color-neighborhood relationships are shown through graph-based color palettes. Experimental results showed that the PointRend method, with a 93.92% success rate, produced the most successful results. However, k-means and GMM methods were more successful in extracting the colors of birds. The evaluation of the palettes was performed with the proposed numerical metric rather than questionnaires, resulting in more robust and repeatable findings. Furthermore, the proposed graph-based color palette results in considerably simplified coloring process by reducing the computational complexity for an industrial product.
dc.identifier.doi10.1109/ACCESS.2025.3553252
dc.identifier.endpage52283
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-105001880250
dc.identifier.scopusqualityQ1
dc.identifier.startpage52270
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2025.3553252
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5897
dc.identifier.volume13
dc.identifier.wosWOS:001455525900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectImage color analysis
dc.subjectBirds
dc.subjectClustering algorithms
dc.subjectAccuracy
dc.subjectColor
dc.subjectImage segmentation
dc.subjectFeature extraction
dc.subjectGenerative adversarial networks
dc.subjectGaussian mixture model
dc.subjectData mining
dc.subjectCIEDE2000
dc.subjectcolor difference
dc.subjectcolor palette extraction
dc.subjectclustering algorithms
dc.subjectHungarian algorithm
dc.titleA Novel Color Difference-Based Method for Palette Extraction and Evaluation Using Images of Birds
dc.typeArticle

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