Over the past year, one of
Shutterstock’s engineering teams has spearheaded and modified computer vision technology to introduce more innovative search and discovery features and to improve the customer’s overall site experience.
In the past, Shutterstock’s search algorithm and similar image offerings on image pages were powered by keywords provided by contributors when they upload their content. That keyword data, while useful for indexing images into categories, isn’t as effective as many customers would like in surfacing the best and most relevant content.
To deal with this issue Shutterstock’s computer vision team worked to apply machine learning techniques to reimagine and rebuild that process.
The technology now relies instead on pixel data within images. It has studied our 70 million images and 4 million video clips, broken them down into their principal features, and now recognizes what’s inside each and every image, including shapes, colors, and the smallest of details; this visual and conceptual data is represented numerically.
Why does that matter? All the data Shutterstock has collected about its content has led to the creation of an unparalleled reverse image search. Customers can now upload an image of their choosing to Shutterstock’s site, bypassing the need to type a query into a search bar, and the technology will identify similar images in
look and feel inside of Shutterstock’s collection. Visual search is available on the
Shutterstock site. Click on the camera in the search bar and upload a photo. You’ll get results based on pixel data instead of the standard keyword data.
Shutterstock will also soon launch visually similar discovery for video. They say they are just getting started with what this technology can do.