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Shutterstock is flexing its AI muscles with news that the stock photo giant is introducing new computer vision search intelligences to its platform.
The company, which is headquartered in New York’s Empire State Building, went public in 2012 and now offers more than 70 million images for bloggers and media, which can make it hard to find. specific assets. Sure, the old keyword search tool is effective to some extent, but what if you want to find similar images to the one you have? Or what if you want alternate images based on color schemes, mood, or shapes? That’s where Shutterstock’s new reverse image search comes in.
Computer vision is essentially a branch of artificial intelligence that allows machines to analyze and understand images by breaking them down and processing them pixel by pixel, rather than by metadata (such as keywords and descriptions which are not only based on human actions, but also on human precision). Shutterstock put together a computer vision team over a year ago, and these are the first fruits of their labor.
Shutterstock’s main search box now offers an option to upload or drag and drop an image.
Choose any image from your PC….
…then Shutterstock starts analyzing the pixels to find matches.
And what you get is a collection of snaps that resemble the original photo, not only in content, but also in appearance.
The underlying concept here is nothing new, of course. Reverse image search is used for multiple purposes by a myriad of services including Snaplay, ImageBrief and TinEye, while the mighty Google also offers a useful reverse image tool.
But companies that provide what appears to be a fairly straightforward technical service now seem to be getting into the realm of machine learning to build better recommendation engines for humans.
Predictive typing keyboard company SwiftKey was recently acquired by Microsoft, not because it has a popular little app for Androids and iPhones, but because it’s building a sophisticated AI-powered back-end. and machine learning. This includes artificial neural networks (ANN) which are more directly based on the structure and functioning of the human brain.
Similarly, Shutterstock has developed its own convolutional neural network for its inverted image technology, something that is also used to enhance its “similar image” option, which is available at the bottom of every image result.
For example, you can see the old keyword-based “similar image” options at the bottom of this English Bulldog photo. Some of the results are bulldogs, sure, but some are just dogs, and some of them are clearly dumb. A dog with a wig is cute, maybe, but is it usable? Actually don’t answer that…
Visually similar new images, while not necessarily an exact match to the original, are more online.
Although Shutterstock is best known for its stock photos, it also has millions of video clips, and the company will soon expand this visually similar search technology to these as well.
“With a collection as large as Shutterstock’s, the importance of being able to identify exactly what a customer needs with advanced search and discovery tools is critical to our continued success,” said Shutterstock Founder and CEO, Jon Oringer. “Doing this in video is a breakthrough, and as technology continues to learn and recognize what’s inside an image or clip, it promises more possibilities. We know we We’ve only scratched the surface of how we use this deep machine learning to better understand and serve our customers.”
From enterprise software and drug discovery to predictive typing and now stock photo searches, machine learning is less of an abstract research area and more of a reality. It can’t be too long before a machine finally beats a human player at Go… wait a minute, Oh.
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