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).
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
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 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
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