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The idea of visual search is certainly not new, says Pawan Sinha, professor of brain and cognitive science at MIT. “Ever since the Web came into being, there has been a large amount of graphical information available,” he says, “and that makes visual search seem like a very attractive idea.” But visual search hasn’t panned out, in part because it’s difficult for a computer to extrapolate context from a photo. For instance, a computer may or may not classify a picture of soldiers raising a flag at Iwo Jima as a World War II event.

Narrowing down the scope of the project to clothing and accessories, Sinha says, helps make the problem more manageable. Still, “it’s a fairly difficult challenge,” he says.

“I think it’s a great idea,” says Sucharita Mulpuru, a senior analyst at Forrester Research. “But I think the big question is how well the algorithm really works–whether or not the product you look for really yields similar results.” She adds that the four categories that features now are “just scratching the surface.” She thinks the concept could have exciting applications beyond clothing and accessories: it could be used to find furniture, rugs, and wallpaper. is a work in progress; it will be tweaked as Shah and his team learn more about how people are using the tool and what they want, he says. And there are still algorithmically challenging aspects of adding shirts to the mix. Shah explains that shirts are usually pictured one of two different ways: either on mannequins or on people, or else lying flat. For computer vision algorithms, it’s difficult to reconcile the two different versions of a shirt. This is a problem that the team is expected to work out in a couple of months, says Shah.

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