Computers may soon be able to tell from your photos which "urban tribe" - hipsters, bikers, surfers or goth - you belong to, scientists say.
Computer researchers at the University of California, San Diego, are developing an algorithm that uses group pictures to determine which of the urban tribes you belong to. So far, the algorithm is 48 per cent accurate on average, researchers said
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.
"This is a first step. We are scratching the surface to figure out what the signals are," 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. This is an extremely difficult problem, Belongie explained, and a 48 per cent accuracy rate is actually a very good result.
One of the researchers' insights was to look at group pictures rather than pictures of individuals. They hoped that this would make it easier to pick up social cues, such as clothing and hairdos, to determine people's tribes based on visuals featuring more than one person.
While humans can recognise urban tribes at a glance, computers cannot. So the algorithm segments each person in six sections - face, head, top of the head (where a hat would be), 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 analyse the picture as the sum of its parts and attributes - in this case
haircuts, hair colour, make up, jewelry and tattoos, for example. The algorithm also analyses the boxes for colour, texture and other factors.
Researchers then let data do the work, feeding the algorithm pictures labelled for the urban tribes they represent - hipsters, surfers, bikers, Goth, etc - a common machine learning technique. Finally, they fed the algorithm pictures without labels.
The computer vision programme accurately determined to which urban tribe the pictures belonged 48 per cent of the time - better than random.
To define urban tribes in the study, computer scientists turned to Wikipedia and selected the eight most popular categories in the encyclopedia's list of subcultures: biker, country, Goth, heavy metal, hip hop, hipster, raver and surfer.
They also included photographs from three common categories for social venues: