How does color extraction work?

The image is drawn to a canvas element, then pixel colors are sampled and grouped into clusters using a simplified k-means algorithm. The most frequent color clusters are returned as the dominant palette.

What image formats are supported?

JPG, PNG, WebP, and GIF images are supported. The image is processed entirely in your browser using the Canvas API — nothing is uploaded.

How many colors should I extract?

Five to ten usually captures a useful palette. Fewer clusters give broad dominant tones for a quick theme; more clusters surface accent colors. Start around 6 and increase if important secondary colors are being merged together.

Why are the extracted colors slightly different from the image?

Each swatch is a cluster centroid — the average of many similar pixels — rather than a single sampled pixel. This produces representative dominant colors instead of noise, so a centroid may sit between several near shades in the photo.

How do I build a brand palette from a logo?

Upload the logo, extract the dominant colors, and copy their HEX values into your design tokens. Logos with flat colors yield very clean results; export the palette as CSS variables or JSON to drop straight into your stylesheet.

Why are dominant colors ordered the way they are?

They are sorted by dominance — the color covering the largest area appears first. That ordering tells you which tones define the image overall feel, useful for choosing a primary background or accent that matches a photo.

Does image resolution affect the result?

Not much. The image is downscaled to about 200×200 before clustering for speed, which preserves the dominant colors while ignoring fine detail. A higher-resolution upload will not meaningfully change the main palette it returns.

Image Color Extractor — Get Colors from Any Image

How to use Image Color Extractor

Extract the dominant color palette from any image — upload a photo, product shot, or design screenshot and instantly get the top 5–10 colors as HEX swatches. Perfect for building a brand palette from a logo, matching a website's color scheme to a hero image, or analyzing the color composition of competitor designs.

Upload an image (JPG, PNG, or WebP) using the file picker or drag-and-drop.
The tool analyzes the pixel data using k-means clustering to find dominant colors.
Review the extracted palette sorted by dominance (largest color area first).
Click any swatch to copy its HEX value.
Export the full palette as JSON, CSS variables, or a swatch PNG.

Your data never leaves your device — 100% private processing.

How dominant color extraction works

The tool samples pixels from the uploaded image (downscaled to ~200×200 for performance), converts them to LAB color space for perceptual accuracy, then applies k-means clustering to find the most representative group centroids. Each centroid represents a dominant color. The number of clusters (5–10) controls palette size. LAB clustering produces more perceptually meaningful groupings than RGB clustering — similar-looking colors end up in the same cluster even if their RGB values differ significantly.

Practical applications

Brand design: upload a company logo to extract its exact hex values for the design system. Product photography: extract dominant colors to auto-tag product listings (e.g., "navy blue sofa") or generate matching background colors. Interior design: upload a room photo to build a complementary paint color palette. Data visualization: extract a natural color palette from a themed photograph for use in chart color scales. Social media: match headline text colors to the dominant colors in a background photo for visually cohesive posts.

Worked examples

Brand logo palette

Inputs: upload a two-color brand logo

Result: dominant swatches as HEX, e.g. #1a73e8 + #fbbc04

Photo palette

Inputs: sunset landscape photo · 6 colors

Result: 6 dominant swatches sorted by area covered

Glossary

K-means clustering: A machine learning algorithm that groups data points into K clusters by minimizing the distance to the nearest centroid.
Dominant color: The color occupying the largest area in an image after clustering nearby shades together.
Color quantization: Reducing the number of distinct colors in an image while preserving its visual appearance — the basis of palette extraction.
Centroid: The central representative point of a cluster — in color extraction, the average color of all pixels assigned to that cluster.

Related Color & Design

Color Converter

Convert colors between HEX, RGB, HSL, HSV, and CMYK formats instantly. Paste any color format and get all others with one click.

Color Palette Generator

Generate beautiful harmonious color palettes online. Choose from complementary, analogous, triadic, and monochromatic schemes. Free color scheme creator.

Color Contrast Checker

Check color contrast ratio for WCAG AA and AAA accessibility compliance. See if your text and background colors meet accessibility standards.

Color Name Finder

Find the name of any color by hex code or color picker. Get the closest CSS color name, Tailwind color name, and nearby alternatives.

Color Name Generator

Find the closest CSS color name from any HEX code, compare RGB distance, and explore similar named colors for design systems and UI consistency.

Color Picker & Converter

Pick a color and convert HEX, RGB, HSL, HSB, and CMYK instantly. Generate tints, shades, and export-ready palette values.

Explore all Color & Design.

Image Color Extractor

How to use Image Color Extractor

How dominant color extraction works

Practical applications

Worked examples

Glossary

Related reading

Frequently Asked Questions

Why use Image Color Extractor?

Common use cases

Get weekly tool tips & updates

Related Color & Design