While Talos is searching for suitable models for the Zombie Formalist, I’ve started experimenting with revisiting the painting appropriation side of the project. For the initial exploration, I’m using da Vinci’s “Mona Lisa” (1517).
The following images are various explorations of abstracting the above image using the SOM to reorganize constituent pixels. Through exploring these I realized that one of the greatest influences on the quality of the result is the random sampling of pixels. The working image is 1080×1607 pixels, which means 1,735,560 training samples. In my tests using ~20,000 training iterations, only a small subset of the diversity of those pixels influence the resulting image. In these tests, I realized the most successful results are those that happen to select (randomly) a large diversity of pixels to train the SOM. The same parameters can produce very different results:
I think the image on the left is more successful because it happened to select a few brighter pixels in the original. I can produced better results by down-scaling the image to increase the diversity of pixels selected by random sampling, but that is not ideal since I’m limiting both the output resolution and the diversity of data used in training. It seems I should stick with the number of iterations that equal (at least) the number of training samples (the number of pixels in the original). Looking again at my old code, I did not realized I had fixed the neighbourhood function; in all the images below, the only variable that effects the output is the number of iterations.