About 130,000 images of apartments, organized by similarity with PCA dimensionality reduction.
Two amazing marigrams (diagrams showing sea level, including tides and waves) from December 23 and 24, 1854. Via the always great NOAA FTP server.
Via eBay, 3lbs of memory chips (that tablecloth!)
An x-ray of an early “silicon grown junction transistor”, made by Morton Jones at Texas Instruments in May of 1954. Via the Smithsonian’s Chip Collection.
For an upcoming Drift Station project, we’ve been considering how to curatorially sort a massive number of images (about 100k) for presentation. Chronological? Random order? Some other logical scheme? But a more computational approach seemed to make sense: some way of parsing the images that took into account a variety of visual factors in each image, something that would be impossible to do manually.
Neural networks are the obvious answer here, and so I found some very helpful sample code from Gene Kogan and Kyle McDonald, and wrote some Python and Processing code that loads up a folder of images and extracts a vector representation from them. Then, using t-SNE and Rasterfairy, the images were organized into a 2D grid.
I’ve spent the last few days playing with settings in the code, and found there is an interesting balance to be struck between locally preserving color similarity and object similarity. (Note: this post is more of a quick note than a deep-dive analysis.)
Above: a version with blurred images, showing a pretty clear separation by color with fairly smooth transitions. Click on images for a higher-res version. Continue reading “Arranging By Color And Objects With t-SNE”
Can’t get enough of this cover from Beginner’s BASIC, from 1979.