Extract dominant colors from any image instantly. Get HEX and RGB values with one click. Free, secure, and runs entirely in your browser.
Supports PNG, JPG, WEBP, GIF. Max 50MB.
Color palette extraction analyzes an image and identifies the dominant or representative colors. The result is a small set of colors — typically 5-8 — that capture the image's visual character. Designers use extracted palettes to coordinate other design elements (UI colors that complement a hero image, brand palettes derived from photography, color schemes that match a mood image).
The extraction uses clustering algorithms (k-means or similar) to group all the image's pixels into the requested number of color clusters. Each cluster's center becomes a palette color. The largest clusters represent dominant colors; the algorithm can also weight by visual importance (saturation, contrast) rather than raw pixel count to surface striking accent colors.
This tool runs in your browser using JavaScript. The image is decoded into pixel data, sampled to reduce computation, and clustered. Output is the palette with hex codes, RGB values, and proportional bars showing each color's prevalence in the image.
Designing pieces that coordinate with existing imagery is much faster with extracted palettes than picking colors by eye. Web designers building landing pages around hero images, marketers producing assets that match a key product photo, and brand designers analyzing competitor imagery all benefit from algorithmic palette extraction.
Palettes also support content management. Categorizing images by dominant color lets you build interfaces where users browse photos, products, or designs by color. Building such a system requires extracting palettes systematically, which programmatic extraction enables.
Upload, choose palette size, get the colors.
K-means clustering iterates: assign each pixel to the nearest cluster center, recompute centers as the mean of assigned pixels, repeat until convergence. Running on full-resolution images is slow; sampling (taking every Nth pixel) accelerates with minimal quality loss.
Color space matters. Clustering in RGB produces palettes weighted by RGB distance, which does not match perceptual distance. Clustering in LAB or HSV produces more perceptually aligned palettes — colors that look similar group together more reliably.
Output: hex codes for each cluster center, plus the percentage of image pixels in each cluster. The percentage represents visual prevalence; the largest cluster is the most-dominant color.