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copied!<p>All they are doing is matching histograms. </p> <p>So build a histogram for your images. Normalize the histograms by size of image. A <strong>histogram</strong> is a vector with as many elements as colors. You don't need 32,24, and maybe not even 16 bits of accuracy and this will just slow you down. For performance reasons, I would map the histograms down to 4, 8, and 10-12 bits. </p> <ul> <li>Do a fuzzy <code>least distance compare</code> between the all the 4 bit histograms and your sample colors. </li> <li>Then take that set and do the 8 bit histogram compare. </li> <li>Then maybe go up to a 10 or 12 bit histogram compare with the remaining set. This will be the highest performance search, because you are comparing the total set with a very small number of calculations, to find a small subset. </li> <li>Then you work on the small subset with a higher number of calculations, etc. </li> </ul> <p>The real big trick is to find the best algorithm for matching similar histograms. </p> <ul> <li><p>Start with the distance calculation. In 3 dimensions i think it was: </p> <p>SQRT((x1-x2)^2 + (y1-y2)^2 + (z1-z2)^2)</p></li> </ul> <p>I'm doing this from memory, so look it up to make sure. </p> <ul> <li><p>For your purposes, you will have more than 3 dimensions, so you will have more terms. A 4 bit histogram would have 16 terms, an 8 bit one would have 256 terms, etc. Remember that this kind of math is slow, so don't actually do the <code>SQRT</code> part. If you normalize the size of your images small enough, say down to 10,000 pixels, then you know you only will ever have to do <code>x^2</code> for values 0..10,0000. Pre-calculate a lookup table of <code>x^2</code> where x goes from 0..10,000. Then your calculations will go fast. </p></li> <li><p>When you select a color from the palette, just make a histogram with that color = 10,0000. When select 2, make a histogram with color1=5000, color2=5000 etc. </p></li> <li><p>In the end you will have to add in fudge factors to make the application match the real world, but you will find these with testing.</p></li> </ul>

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