Google Unveils Neural Network with "Superhuman" Ability to Determine the Location of Almost Any

["Dave Farber" <dave () farber net>]

---------- Forwarded message ---------
From: Dewayne Hendricks <dewayne () warpspeed com>
Date: Mon, Feb 27, 2017 at 2:01 AM
Subject: [Dewayne-Net] Google Unveils Neural Network with "Superhuman"
Ability to Determine the Location of Almost Any Image
To: Multiple recipients of Dewayne-Net <dewayne-net () warpspeed com>


Google Unveils Neural Network with “Superhuman” Ability to Determine the
Location of Almost Any Image
Guessing the location of a randomly chosen Street View image is hard, even
for well-traveled humans. But Google’s latest artificial-intelligence
machine manages it with relative ease.
 By Emerging Technology from the arXiv
Feb 24 2017
<
https://www.technologyreview.com/s/600889/google-unveils-neural-network-with-superhuman-ability-to-determine-the-location-of-almost/
>

Here’s a tricky task. Pick a photograph from the Web at random. Now try to
work out where it was taken using only the image itself. If the image shows
a famous building or landmark, such as the Eiffel Tower or Niagara Falls,
the task is straightforward. But the job becomes significantly harder when
the image lacks specific location cues or is taken indoors or shows a pet
or food or some other detail.

Nevertheless, humans are surprisingly good at this task. To help, they
bring to bear all kinds of knowledge about the world such as the type and
language of signs on display, the types of vegetation, architectural
styles, the direction of traffic, and so on. Humans spend a lifetime
picking up these kinds of geolocation cues.

So it’s easy to think that machines would struggle with this task. And
indeed, they have.

Today, that changes thanks to the work of Tobias Weyand, a computer vision
specialist at Google, and a couple of pals. These guys have trained a
deep-learning machine to work out the location of almost any photo using
only the pixels it contains.

Their new machine significantly outperforms humans and can even use a
clever trick to determine the location of indoor images and pictures of
specific things such as pets, food, and so on that have no location cues.

Their approach is straightforward, at least in the world of machine
learning. Weyand and co begin by dividing the world into a grid consisting
of over 26,000 squares of varying size that depend on the number of images
taken in that location.

So big cities, which are the subjects of many images, have a more
fine-grained grid structure than more remote regions where photographs are
less common. Indeed, the Google team ignored areas like oceans and the
polar regions, where few photographs have been taken.

Next, the team created a database of geolocated images from the Web and
used the location data to determine the grid square in which each image was
taken. This data set is huge, consisting of 126 million images along with
their accompanying Exif location data.

Weyand and co used 91 million of these images to teach a powerful neural
network to work out the grid location using only the image itself. Their
idea is to input an image into this neural net and get as the output a
particular grid location or a set of likely candidates.

They then validated the neural network using the remaining 34 million
images in the data set. Finally they tested the network—which they call
PlaNet—in a number of different ways to see how well it works.

The results make for interesting reading. To measure the accuracy of their
machine, they fed it 2.3 million geotagged images from Flickr to see
whether it could correctly determine their location. “PlaNet is able to
localize 3.6 percent of the images at street-level accuracy and 10.1
percent at city-level accuracy,” say Weyand and co. What’s more, the
machine determines the country of origin in a further 28.4 percent of the
photos and the continent in 48.0 percent of them.

That’s pretty good. But to show just how good, Weyand and co put PlaNet
through its paces in a test against 10 well-traveled humans. For the test,
they used an online game that presents a player with a random view taken
from Google Street View and asks him or her to pinpoint its location on a
map of the world.

[snip]

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