Google's Image Searching Gets Even Better

 Computer Vision  

Google has explained their new award-wining image detection system that can identify multiple objects in a scene, even if they're partly obscured. The key is a neural network that can rapidly refine the criteria it's looking for without requiring a lot of extra computing power.

During the annual ImageNet Computer Vision competition this year, the winning techniques continued the exponential progress of blowing last year's entries out of the water.

John Markoff of the New York Times published a piece on competition and some of those improvements recently.

“We see innovation and creativity exploding,” said Fei-Fei Li, the director of the Stanford Artificial Intelligence Laboratory and one of the creators of a vast set of labeled digital images that is the basis for the contest. “The algorithms are more complex and they are just more interesting.”

"These technological advances will enable even better image understanding on our side and the progress is directly transferable to Google products such as photo search, image search, YouTube, self-driving cars, and any place where it is useful to understand what is in an image as well as where things are."

In the five years that the contest has been held, the organizers have twice, once in 2012 and again this year, seen striking improvements in accuracy, accompanied by more sophisticated algorithms along with larger and faster computers.

Now, Google has published a blog post explaining some of their techniques including, deep learning networks. The team of researchers used the methods to win in a few different categories at the competition.

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The deeper scanning system Google used can both identify more objects and make better guesses including items in a living room, and in one example, a jumping cat.

Despite the incredible increases in computer vision accuracy, the systems still cannot match human vision, according to the researchers, and there is a lot of progress remaining to equal a human looking at an image.

According to the post, "These technological advances will enable even better image understanding on our side and the progress is directly transferable to Google products such as photo search, image search, YouTube, self-driving cars, and any place where it is useful to understand what is in an image as well as where things are."


SOURCE  Google Research

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