Dominant Color Extraction with K-Means
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Dominant color extraction using K-means is a valuable technique for various applications like image search, color editing, and palette generation. Traditional methods struggle to extract dominant colors from small regions, crucial for analyzing color schemes. Studies have applied K-means clustering to extract dominant colors from images of metropolitan cities, showcasing similarities in dominant colors among cities like Mexico City and New York City, which share eerie black and platinum. Another approach, the Patch Adaptive Selection with K-Means (P-ASK) framework, efficiently extracts dominant colors from digital images, reducing training sample size and improving color adjustment accuracy. An improved K-means algorithm has been developed for color extraction, utilizing an HSV space model and color similarity threshold values for effective clustering. These methods collectively demonstrate the significance and versatility of K-means in extracting dominant colors for various practical applications.
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| Papers (5) | Insight |
|---|---|
| The paper presents a color extraction method using an improved K-means algorithm, identifying dominant colors by clustering points with high color similarity thresholds in the HSV space model. | |
01 Jan 2020 14 Citations | The paper utilizes K-Means clustering algorithm for efficient extraction of dominant color palettes from images, enabling reconstruction based on these palettes. |
7 Citations | The paper introduces Patch Adaptive Selection with K-Means (P-ASK) for dominant color extraction in camera characterization, reducing training sample size and improving color adjustment accuracy. |
2 Citations | The paper proposes a method for dominant color extraction using K-means in the CIELAB color space, enabling extraction of prominent colors from small image regions effectively. |
| Not addressed in the paper. |
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