Algorithm for determining the prominant colour of a photograph

When we look at a photo of a group of trees, we are able to identify that the photo is predominantly green and brown, or for a picture of the sea we are able to identify that it is mostly blue.

Does anyone know of an algorithm that can be used to detect the prominent color or colours in a photo?

I can envisage a 3D clustering algorithm in RGB space or somethin开发者_如何学Gog similar. I was wondering if someone knows of an existing technique.

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Convert the image from RGB to a color space with brightness and saturation separated (HSL/HSV) http://en.wikipedia.org/wiki/HSL_and_HSV

Then find the dominating values for the hue component of each pixel. Make a histogram for the hue values of each pixel and analyze in which angle region the peaks fall in. A large peak in the quadrant between 180 and 270 degrees means there is a large portion of blue in the image, for example.

There can be several difficulties in determining one dominant color. Pathological example: an image whose left half is blue and right half is red. Also, the hue will not deal very well with grayscales obviously. So a chessboard image with 50% white and 50% black will suffer from two problems: the hue is arbitrary for a black/white image, and there are two colors that are exactly 50% of the image.

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It sounds like you want to start by computing an image histogram or color histogram of the image. The predominant color(s) will be related to the peak(s) in the histogram.

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You might want to change the image from RGB to indexed, then you could use a regular histogram and detect the pics (Matlab does this with rgb2ind(), as you probably already know), and then the problem would be reduced to your regular "finding peaks in an array".

Then n = hist(Y,nbins) bins the elements in vector Y into 10 equally spaced containers and returns the number of elements in each container as a row vector.

Those values in n will give you how many elements in each bin. Then it's just a matter of fiddling with the number of bins to make them wide enough, and with how many elements in each would make you count said bin as a predominant color, then taking the bins that contain those many elements, calculating the index that corresponds with their middle, and converting it to RGB again.

Whatever you're using for your processing probably has similar functions to those

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1. Average all pixels in the image.
2. Remove all pixels that are farther away from the average color than standard deviation.
3. GOTO 1 with remaining pixels until arbitrarily few are left (1 or maybe 1%).

You might also want to pre-process the image, for example apply high-pass filter (removing only very low frequencies) to even out lighting in the photo — http://en.wikipedia.org/wiki/Checker_shadow_illusion