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I have the following image:

enter image description here

What I want to do is "id" the individual strips based on their dominant color. What is the best approach to do this?

What I've done is used the image's value (HSV) and make a distribution on that value's occurrence. The problem is, for strip0 values [27=32191, 28=5433, others=8] strip1 values [26=7107, 27=23111, others=22]. I can't get a definitive distinction.

The project's main goal is to compare an actual yellow-colored paper to the strips and determine which strip is the most similar.

Zombo
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user2034438
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  • Do you want to solely separate the strips or you want to do something else ? – mmgp Feb 02 '13 at 03:28
  • I don't want to separate the strips. The image is for reference. Say I have this image http://i.imgur.com/qn2AAJp.jpg?1. In which strip is it most similar? I should have mentioned that earlier. Sorry. – user2034438 Feb 02 '13 at 03:57
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    I wonder if you really want the dominant colour? Maybe a high-precision average would be appropriate. – Joel Feb 02 '13 at 05:58
  • @Joel, what's a high precision average? i assume this is in comparison to low precision average... – thang Feb 02 '13 at 20:46

4 Answers4

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First, since you know the boundaries of each strip in the reference image, the only problem possible here is that your reference image is noisy. A relatively overkill way to handle that is clustering the colors in each strip and taking the cluster's centroid as the representative color of the strip. In order to get a more meaningful response here, consider the CIELAB colorspace for this step. Doing this, and converting the results back to RGB, for the first strip I get the rgb triplet (0.949375, 0.879872, 0.147898), and for the second strip (0.945324, 0.857322, 0.129756) (each channel in range [0, 1]).

When you get a new image, you perform the same operation. But there are a lot of problems here. For instance, how are you handling the white balance in this input image ? Supposing you have no such problem, then now it is only a matter of finding the nearest color to the one you just found by the same process. To find the nearest color you have to use a meaningful colorspace for such thing too, and CIELAB is recommended again since the well established Delta-E functions are defined on it. See http://en.wikipedia.org/wiki/Color_difference for some such metrics, the simplest being the euclidean distance in CIELAB.

mmgp
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Calibrate your equipment. If you do not calibrate your equipment, you will have arbitrary errors between the test sample and the reference. Lighting is part of your equipment.

Use edge detection and your knowledge of the reference strip's geometry (strips are equal width) to determine sampling regions. For each sampling region, extract an internal patch.

For the test strip, compute an image where each pixel is the max difference within a sampling window (e.g. 5x5). This will let you identify a relatively homogeneous region which is dissimilar to the outside region (i.e. the paper). Extract a patch.

Use downsampling to find an integrated color for each patch per svnpenn's advice. You can look at other computation methods later, but this should work quite well.

For weights wh, ws, wv, compute similarity = whabs(h0-h1) + wsabs(s0-s1) + wv*abs(v0-v1) between the test color and each reference color. You can look at other distance measures later, but this should work quite well. Start with equal weights. One perk to this method is that it behaves well regardless of the dimension or combination of dimensions under which the reference strip varies.

Sort the results to find the most similar and second most similar matches. Note that similarity is set up so zero is an exact match, and a big number is a poor match. Use the ratio of these two results to estimate the quality of the most similar match - if the first two matches are very close, it's probably not a great match to either.

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Jim Gray
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You can scan through all the colors and use a hashtable to keep track of how many pixels of each color there are.

Take those numbers and, remembering which colors they correspond to, sort them in decreasing order.

Look at the sorted list of numbers and find the difference between each consecutive pair of numbers. Keep track the indices in the list of the two numbers that resulted in each difference. Sort this difference list.

Look at the maximum number in the difference list. You now have the biggest drop-off between two sets of pixels. Go find which was the bigger one. Everything with this number of pixels and above is a dominant color. Everything below is a sub-dominant color. Now you know how many dominant colors you have, and what they are.

Should be pretty easy from there to do whatever it is you want to do.

The only time this wouldn't work is if some of the noise was of the same color as a strip, so much so that it corrupted your data.

In this case, you would use a different approach, which you can also use in the first case - looking at runs. Go through the pixels, and each time you find a new color, look at how many of the following pixels are of the same color.

Use the method described earlier to cluster the colors into dominant and non-dominant, for the same result.

In both cases, if you know that the picture is of vertical strips, you could limit the number of horizontal lines of colors you look at to make things go faster.

Andrew Latham
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  • This is really what I want to achieve. "I don't want to separate the strips. The image is for reference. Say I have this image i.imgur.com/qn2AAJp.jpg?1. In which strip is it most similar? I should have mentioned that earlier. Sorry" – user2034438 Feb 02 '13 at 04:06
  • Okay, do a scan through the image of all the RGB or HSV values. Find the average for each of the three values and map that to a 3-D grid with axes R,G,B or H,S,V. Compute the Euclidean distance from each of the dominant colors you have to choose from and go with the closest color. – Andrew Latham Feb 02 '13 at 04:17
  • Will that be to expensive? with the Euclidean distance? After determining the algorithm, we still have to load it to a DSP. Basically in an embedded system. We're still not sure how fast our hardware is. – user2034438 Feb 02 '13 at 04:22
  • If there are k colors, the Euclidean distance will be O(k) for each image after the preprocessing of the strips. However, if you wanted to get really complicated you could make a kd-tree. The main place for optimization here is in finding the dominant color of the image you're examining. – Andrew Latham Feb 02 '13 at 04:27
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You could split the image into sections, then resize each section to one pixel. This is an example using the whole image

$ convert Y82IirS.jpg -resize 1x1 txt:
# ImageMagick pixel enumeration: 1,1,255,srgb
0,0: (220,176, 44)  #DCB02C  srgb(220,176,44)

Average colour of an image

Zombo
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