New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of Hues
Dominant colors significantly influence visual image perception and are widely used in computer vision and design. Traditional extraction methods often neglect visually salient colors that occupy small areas yet possess high aesthetic relevance. This study introduces a method for detecting both domi...
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MDPI AG
2025-06-01
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| author | Yurii Kynash Mariia Semeniv |
| author_facet | Yurii Kynash Mariia Semeniv |
| author_sort | Yurii Kynash |
| collection | DOAJ |
| description | Dominant colors significantly influence visual image perception and are widely used in computer vision and design. Traditional extraction methods often neglect visually salient colors that occupy small areas yet possess high aesthetic relevance. This study introduces a method for detecting both dominant and visually prominent colors in a wide range of hues and images. We analyzed the color gamut of images in the CIE L<sup>*</sup>a<sup>*</sup>b<sup>*</sup> color space and concluded that it is difficult to identify the dominant and prominent colors due to high color variability. To address these challenges, the proposed approach transforms images into the orthogonal ICaS color space, integrating the properties of RGB and CMYK models, followed by K-means clustering. A spectral residual saliency map is applied to exclude background regions and emphasize perceptually significant objects. Experimental evaluation on an image database shows that the proposed method yields color palettes with broader gamut coverage, preserved luminance, and visually balanced combinations. A comparative analysis was conducted using the ΔE<sub>00</sub> metric, which accounts not only for differences in lightness, chroma, and hue but also for the perceptual interactions between colors, based on their proximity in the color space. The results confirm that the proposed method exhibits greater color stability and aesthetic coherence than existing approaches. These findings highlight the effectiveness of the orthogonal saliency mean method for delivering a more perceptually accurate and visually consistent representation of the dominant colors in an image. This outcome validates the method’s applicability for image analysis and design. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Technologies |
| spelling | doaj-art-dd954996110b4f24b1a408ff224ea48d2025-08-20T03:26:52ZengMDPI AGTechnologies2227-70802025-06-0113623010.3390/technologies13060230New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of HuesYurii Kynash0Mariia Semeniv1Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, 79013 Lviv, UkraineInstitute of Computer Science and Information Technologies, Lviv Polytechnic National University, 79013 Lviv, UkraineDominant colors significantly influence visual image perception and are widely used in computer vision and design. Traditional extraction methods often neglect visually salient colors that occupy small areas yet possess high aesthetic relevance. This study introduces a method for detecting both dominant and visually prominent colors in a wide range of hues and images. We analyzed the color gamut of images in the CIE L<sup>*</sup>a<sup>*</sup>b<sup>*</sup> color space and concluded that it is difficult to identify the dominant and prominent colors due to high color variability. To address these challenges, the proposed approach transforms images into the orthogonal ICaS color space, integrating the properties of RGB and CMYK models, followed by K-means clustering. A spectral residual saliency map is applied to exclude background regions and emphasize perceptually significant objects. Experimental evaluation on an image database shows that the proposed method yields color palettes with broader gamut coverage, preserved luminance, and visually balanced combinations. A comparative analysis was conducted using the ΔE<sub>00</sub> metric, which accounts not only for differences in lightness, chroma, and hue but also for the perceptual interactions between colors, based on their proximity in the color space. The results confirm that the proposed method exhibits greater color stability and aesthetic coherence than existing approaches. These findings highlight the effectiveness of the orthogonal saliency mean method for delivering a more perceptually accurate and visually consistent representation of the dominant colors in an image. This outcome validates the method’s applicability for image analysis and design.https://www.mdpi.com/2227-7080/13/6/230image preprocessingimage segmentationcolor in image processingprominent colorscolor palettek-means clustering |
| spellingShingle | Yurii Kynash Mariia Semeniv New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of Hues Technologies image preprocessing image segmentation color in image processing prominent colors color palette k-means clustering |
| title | New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of Hues |
| title_full | New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of Hues |
| title_fullStr | New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of Hues |
| title_full_unstemmed | New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of Hues |
| title_short | New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of Hues |
| title_sort | new approach to dominant and prominent color extraction in images with a wide range of hues |
| topic | image preprocessing image segmentation color in image processing prominent colors color palette k-means clustering |
| url | https://www.mdpi.com/2227-7080/13/6/230 |
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