#5 Finalization Revisit

Posted: February 29, 2020 at 9:34 pm

I realized working on finalizing #07, that the code was not using the Gaussian neighbourhood function as I had been previously using so I did did #5. The best result (on top left) is quite similar to the previous result (top right), but a little less smooth. I think the top left is a strong result. I’ve also included the other explorations using the appropriate Gaussian function in the gallery below. The training process for the Gaussian function is slower (since the fall off of learning effect is quite steep).

Zombie Formalist Images on Meural!

Posted: February 25, 2020 at 1:02 pm

Early in 2019 I was invited by the Lumen Prize to submit some new work to be available on the Meural digital art frame. I submitted some early sketches for the Zombie Formalist but did not hear back. In Googling the Zombie Formalist (as one does) I found that my submission was accepted and is available here for Meural!

Twitter Response to “Bad” Compositions.

Posted: February 25, 2020 at 12:48 pm

I uploaded a random sampling of 108 “bad” compositions to Twitter, following the “good” compositions from this post using the same A-HOG data set. The “bad” set has a marginally lower mean number of likes (0.52), but more than double the mean retweets (0.44). The total number of likes for the “bad” set was 56 (compared to 68 for the “good” set); the total number of retweets for the “bad” set was 48 (compared to only 19 for the “good” set). Of course an uncontrolled variable is the size of the growing twitter audience for the Zombie Formalist. Following is a plot analogous to this post. I’ve also included the compositions from this set with the most likes and retweets (corresponding to the 5 peaks below)


Ruling out #7

Posted: February 23, 2020 at 12:43 pm

After working through a few variations, see below, I was unable to get #7 to look smooth; the ‘camo’ aesthetic persists, even with much smaller learning rates and more iterations. I’ve decided to remove this from the running for the final selections.

I’ve also included an interesting error here for future reference, shown below. This occurred when I used a learning rate of 2 (where the max should be 1), which caused the neighbourhood function to wrap around in the middle of each neighbourhood. This causes an interesting aesthetic that reminds me of photography through water droplets on glass where spots of focus (lack of re-organization) punctuate areas of order (re-organization) due to lensing effects.

Initial Twitter Response to “Good” Compositions.

Posted: February 11, 2020 at 4:06 pm

I uploaded 110 “good” compositions to Twitter; “Good” was defined by thresholding (> 50) the attention (number of frames where faces are detected) for each composition generated in the last (A-HOG) integrated test. The max number of likes was 6 and the max retweets 2. The mean likes was 0.62 and the mean retweets was 0.17. The following plot shows the likes (red), retweets (green) and their sum (blue) on the y axis for each composition (x axis). The peaks in the sum indicate one very successful composition (6 likes + 2 retweets) and 5 quite successful compositions. These compositions are included in the gallery below.


#5 Finalization

Posted: February 11, 2020 at 8:18 am

The image on the top is the best of these final explorations. I went through a few more iterations than I was expecting to; I learned a lot more through the process of going through the painting long-list and spending more time on the early entries makes sense. The image on the top is the final selection and the gallery below the explorations.