Growing album is a video of 11k album covers growing in saturation and complexity. The idea is to show a ‘growth pattern’ of sorts in album cover design.
The image visualization project was a series of trials and errors for me. I started off with he fingerprint images data. I had a collection of 7000 image sets. I sorted them by type of fingerprint: Arches, Loops and Whorls, based on the metadata I had. The only other meta-data I had was the gender of these fingerprints. The idea was to build portraits using these fingerprints and point out genders. For example, making a fingerprint portrait of President Obama, and using 56% female fingerprints, because 56% of the people who voted for him were women.
However, this project seemed less cultural. There was less ‘truth’ in it, and less of a people’s factor. I was advised to look into a different image data set and I did. I started looking at an image set of 1 mn audio covers.
So I had a whole new data set of beautiful 1 million audio record covers. I first cleaned the image-set to remove all he junk images. Then I used ofxTSNE algorithm by Gene Kogan to make a grid of these covers sorting the visually similar images. I did multiple small grids with sets of images from the big image-set. This is the TSNE grid I got from all the images. It very strangely resembles a wolf when zoomed out.
Zoomed in versions of the TSNE:
I recognized several visual patterns in these album cover designs. There were shape patterns like circular disks. Several album covers had a group of people, most commonly the band members, sometimes the crowd. Some audio album covers had a single person with a guitar or another musical instrument. A group of images were just very intricate designs or ornate patterns. It was interesting to observe the change in lighting in different album covers.
I then attempted to run a web search algorithm on each image to associate some sort of meta-data to it, like the genre, or time. The aim was to add audio files to each image. But I failed in doing this because I could not find a lot of these images and scraping audio was difficult for me. So I reverted back to trying to sort these images. One sorting that excited me was intricacy of the album cover design. I used average pixel color variation from the median color of the image, and used this standard deviation measure to determine the more complex images vs the less complex. Another sorting I did was based on the image saturation. This was interesting because I saw a pattern in genres when I did the saturation sorting. A lot of classical music and jazz appeared in the less saturated images. A lot of Jazz also appeared in the night images. A lot of international music, like Indian or Japanese music appeared in the most saturated brackets. I then arranged these images in a video and associated the Shepard sound note to them, to produce a progressive music effect, to show an increase. The Shepard tone gives the audio illusion of being an increasing tone, but in reality it’s really a sound consisting of a superposition of sine waves separated by octaves. The idea was to show an ‘increase’. This video has a set of 11,000 images.
I received a good amount of criticism and feedback for this. A major one was that, I am trying to show a change, but a video is not the best way to do it, because you can see the slow progression, but not the change over time. A static visualization with the images arranged in sequence would have made for a better way to depict this ‘variety’ in images.
Kyle McDonald for an introduction to working with large image datasets.
Golan for the guidance.
Colleagues and guests at the critique session for feedback.