In their paper, “Who Are the Devils Wearing Prada in New York City?” KuanTing Chen and four colleagues describe their approach to designing a way to automatically detect fashion trends in New York City. They began by collecting two buckets of data: 471 “street-chic images” in New York from April 2014 to July 2014, and 575 street-chic images in New York from April to July 2015; and 3,914 images from the 2014 summer/spring New York Fashion Week, and 4,000 images from the 2015 summer/spring New York Fashion Week.
The team trained their machine vision algorithm to recognize a person’s pose and nine regions of their body: upper arms, lower arms, upper legs, lower legs, and torso. “It then analyses the color, texture, skin, and so on for each of these areas to create a list that acts like a visual feature vector for the entire body,” according to MIT Technology Review.
So what did the trained algorithm find in its initial analyses of the data sets? “For example, in upper body color, both 2014 and 2015 fashion images have a large amount of white, gray and black colors,” according to the paper. The team adds that in 2015, blue, cyan, red, and multicolor garments are popular for the upper body, while blue and purple are popular for the lower body.