Researchers at Cornell University applied data-mining technology to determine the global variance of clothing styles from 100 million Instagram photos taken in 44 cities.
The team used a standard face-recognition program to screen out certain qualities, leaving 15 million images of people showing the upper half of their body, along with their location and the date.
The team trained a machine-learning algorithm to recognize various types of clothing and accessories, and then had it sift through the photo dataset while another algorithm searched for clusters of images with similar visual themes and tracked how these varied across time and between locations.
The clustering algorithm identified about 400 distinct visual themes, whose variation by time and place could be analyzed.
"The combination of big data, machine learning, computer vision, and automated analysis algorithms would make for a very powerful analysis tool more broadly in visual discovery of fashion and many other areas," the researchers note.
From Technology Review
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