Color extraction is the process of identifying and isolating specific colors from an image or visual data. This can be done for various purposes, such as design, art, data analysis, or machine learning. Here’s a detailed description of the color extraction process:

Input Image: The process begins with an input image from which colors need to be extracted.

Color Space Conversion: Images are usually in the RGB (Red, Green, Blue) color space. Depending on the requirements, the image might be converted to other color spaces like HSV (Hue, Saturation, Value), Lab, or CMYK, which can make it easier to isolate specific colors.

Color Quantization: This step reduces the number of distinct colors in an image, making it easier to identify the dominant colors. Techniques like K-means clustering, median cut, or octree can be used for quantization.

Color Detection: Specific colors are identified based on their values in the chosen color space. For example, in the RGB color space, colors can be detected using their RGB values, whereas in the HSV color space, colors can be detected using their hue, saturation, and value.

Mask Creation: A binary mask is created to isolate the areas of the image that contain the target colors. Pixels matching the target color criteria are set to one value (e.g., white), and all other pixels are set to another value (e.g., black).