We had to do this at Polyvore (a fashion e-commerce site) to extract dominant colors from products.
To create the palette, we took 10k random product images from our catalog, cropped them to the center 1/3, and stitched them into a single image collage. Then we used imagemagick to quantize it to 64 colors to produce our palette.
The extract colors for a given image, we’d crop to center, quantize using the above palette and then run the histogram.
The extracted colors were used in a few different ways:
1. product search - show me "red" shirts.
We also mapped certain hues to "saturated", "muted", "pastel" etc... meta colors based on HSV values. Unfortunately, we never got to surfacing these meta colors as search facets.
2. similar product recommendations - show me shirts similar to this one. colors (and meta colors like "muted") were one of the features used in similar product recs.
Comments
We had to do this at Polyvore (a fashion e-commerce site) to extract dominant colors from products.
To create the palette, we took 10k random product images from our catalog, cropped them to the center 1/3, and stitched them into a single image collage. Then we used imagemagick to quantize it to 64 colors to produce our palette.
The extract colors for a given image, we’d crop to center, quantize using the above palette and then run the histogram.
That's interesting. What was the use case? (why extracting colors?)
The extracted colors were used in a few different ways:
1. product search - show me "red" shirts.
We also mapped certain hues to "saturated", "muted", "pastel" etc... meta colors based on HSV values. Unfortunately, we never got to surfacing these meta colors as search facets.
2. similar product recommendations - show me shirts similar to this one. colors (and meta colors like "muted") were one of the features used in similar product recs.
3. analytics - turquoise is "trending"