We send messages about our lifestyle choices everyday through our clothes, hairstyle, cars, homes, facial expressions and body language.
And thanks to our obsession with photo documentation of nearly every event in our lives, no matter how small, advertisers may soon know even more about us than what is being revealed by our computer browsers and Internet activity, even if you’re a hipster.
Researchers at the University of California, San Diego, are developing a computer vision program that scans photos to determine an individual’s urban tribes, or in-groups.
Based on a list of subcultures found on Wikipedia, the scientists narrowed down the categories to include hipsters, ravers, bikers, country, heavy metal, goth, surfers and hip-hop.
The algorithm is able to determine to which category the photo belongs nearly 50% of the time, much higher than random guesses with 10% accuracy rates. Now they intend to test it against human accuracy.
Computer vision is concerned with modeling and replicating human vision using computer software and hardware. Image understanding can be seen as the disentangling of symbolic information from image data. Development of the field involves duplicating the abilities of human vision by electronically perceiving and understanding an image.
While humans can recognize urban tribes at a glance, computers cannot. So the algorithm segments each person in six sections—face, head (and top of head), neck, torso and arms. This method is an example of what’s better known as a “parts and attributes” approach.
Computer scientists designed the algorithm to analyze the picture as the sum of its parts and attributes—in this case haircuts, hair color, make up, jewelry and tattoos, for example. The algorithm also analyzes the boxes for color, texture and other factors.
An algorithm able to identify people’s urban tribes would have a wide range of applications, from generating more relevant search results and ads, to allowing social networks to provide better recommendations and content.
There also is a growing interest in analyzing footage from cameras installed in public spaces to identify groups rather than individuals. Computer scientists presented their findings at the British Machine Vision Conference in the United Kingdom this fall.
“This is a first step,” said Serge Belongie, a computer science professor at the Jacobs School of Engineering at the University of California, San Diego, and co-author of the study. “We are scratching the surface to figure out what the signals are.”
Hipsters with anti-commercialist leanings sample different styles and identities in opposition to consumer culture. It is somewhat ironic that those same cultural tokens and props are co-opted and sold to them by advertisers.