The markov chain like system I set up for words starts producing really really good results once there are hundreds and hundreds of source words.
It made me interested in doing the same thing with pictures. Take a database of a few thousand pictures of the same thing, faces for example, and see how good the system is at generating new ones.
Pixel by pixel a new image could form. Pixels would be added in a random order so that there isn't any bias towards a direction, and later pixels could be less random and focus more on only the best output.
Hardest part would be figuring out a "scoring" system. My word system is fairly basic and just checks the quantity of matching letters. This system would need to go pixel by pixel and search in a circle for similar colors. Find the best match based off similarity and distance. Then add up the scores for all already defined pixels.
Do it in black and white first, and then deal with color later. Would not work to do rgb separately.
It made me interested in doing the same thing with pictures. Take a database of a few thousand pictures of the same thing, faces for example, and see how good the system is at generating new ones.
Pixel by pixel a new image could form. Pixels would be added in a random order so that there isn't any bias towards a direction, and later pixels could be less random and focus more on only the best output.
Hardest part would be figuring out a "scoring" system. My word system is fairly basic and just checks the quantity of matching letters. This system would need to go pixel by pixel and search in a circle for similar colors. Find the best match based off similarity and distance. Then add up the scores for all already defined pixels.
Do it in black and white first, and then deal with color later. Would not work to do rgb separately.
