1. Start with a random assortment of 300 photos from the larger the list (of 20,000 photos).

2. For each unselected photo, A from the set of 20000

3. For each B of the 200 selected photos

4. Swap A and B.
If the swap produces a better match to the target image, keep it.

I repeat from step 2 until no progress can be made.

The “better match” is the same type of matching used for photo mosaics. I compute the sum of the RGB differences for each pixel. Lower sums are closer matches.

I assume you “carefully choose” the 300 photos using a program, correct (not manually)? What’s the algorithm like?

I have an array of integers which represent the sums of the R,G,B components of each pixel. For each image, I add it’s value to the array. So, for the pixel at 0,0, given 3 images, I might add the following values:

Image 1: rgb(0,0) = 50, 60, 50
Image 2: rgb(0,0) = 62, 72, 100
Image 3: rgb(0,0) = 200, 198, 170
————
This produces: 312, 338, 320

After I’ve added all the images. I then scale the numbers (“normalize” them) so that the lowest value maps to 0, and the highest value maps to 255.