I want to create an image made up of many separate images of similar but different flowers. I want to select the ones that are the closest in overall color to each other. I can do this by just looking at them, but I wondered if there might be a formula I could use to assess them mathematically.

The IV Histogram feature is handy for finding the average RGB values over a selected area. If I do that for each image, is there a formula that will allow me to calculate some sort of difference between each image and some target?

In the table below, the target RGB values are in row 4 (100 150 200 = blue). In rows 6-12, various RGB values are compared. Column F has a simple sum of differences. Column G has a sum of the squares of those differences (least squares).

R/C C D E F G H 3 R G B Comments 4 100 150 200 Target values 5 R G B Sum(Diff) Sum(Sqrs) 6 100 150 200 0 0 =Target 7 200 250 0 0 245 Reverse of target 8 101 151 201 +3 2 +1 each 9 99 149 199 -3 2 -1 each 10 101 149 201 +1 2 +1 or -1 each 11 130 150 200 +30 30 +30 in R 12 110 160 210 +30 17 +10 each 13 110 140 210 +10 17 +10 -10 +10

Row 6 has the target values and both errors are zero.

Row 7 has R & G at +100 and B at -200, so the sum of differences (F7) is zero. I think this disqualifies that method.

Rows 8-10 play with differences of 1 (plus or minus).

Rows 11-13 play with a total difference of 30 either all in one value or spread around.

The sum of differences seems like a crude measure. The sum of squares seems somewhat better.

Is there a metric that I can (easily) apply to a collection of images to determine whine ones will appear to the eye as the most similar in overall color?

Thanks