[A] new algorithm developed at Brown University and the Technical University of Berlin [is] the first computer application designed for “semantic understanding” of abstract drawings, and the research team says it could improve search applications and sketch-based interfaces…
The program can identify simple abstract sketches 56 percent of the time, compared to humans’ 73 percent average. Even those sorely lacking in verisimilitude can be detected, which is the key breakthrough here.
Computers can already recognize accurate sketches, like a police sketch of a suspect compared to photos of a face, for example. But for the type of abstract sketches we all grow up with, it’s a different challenge.
[I]f you’re asked to draw a rabbit, you would probably draw something with buck teeth, huge ears and exaggerated whiskers. Other people would easily recognize this cartoonish representation… But it doesn’t actually resemble the real thing in any meaningful way, so a computer would have no idea what it is …there are subtle tricks and meanings that a human can distinguish, but which present a tough challenge for something built in the black-and-white, ones-and-zeroes world.
[Researchers] devised a list of everyday things people might feel like doodling, settled on 250 categories and used Amazon’s Mechanical Turk crowdsourcing platform to hire some sketch artists. They took 20,000 unique sketches and fed them into existing machine-learning algorithms to train the system. The project culminated in a fun real-time computer Pictionary, where the system tries to recognize objects as the person draws them.
These are drawings of dogs, and what the computer thought they actually were.
To expand their data set, the team is thinking about gamifying this concept into something you can play on iOS or Android devices…
The goal would be improved sketch-based search, the researchers say. That could improve computer accessibility for speech, movement or literacy-impaired people — and it could work in any language, too.