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Computer Scientists Scour Your Holiday Photos
Barence writes "Hundreds of thousands of images on Flickr are being used to teach a program to determine the geographic location of an image, simply by looking at it. The program attempts to mimic the way that humans can deduce the location of an image by searching for visual clues, such as similarities to pictures or locations they have seen previously. In its current state it can guess the location of a photo to within 200km, 16% of the time — extremely accurate given the complexity of the problem."
... is a program that will remember the names of the people in the photos.
Intron: the portion of DNA which expresses nothing useful.
then... if there are 6 sources of pictures, by blindfold guessing you'll get it right 16.66..% of the time
Dawkins Revisited: A person is shit's way of making more shit -- Steve Barnett, anthropologist.
200km, 16% of the time? I guess that sounds sorta neat... except that 84% of the time, it's off by more than 200km. Now, we know that the earth's circumference is 40000km, and it follows that nobody can ever be more than 20000km from any location on Earth.
So 16% of the time, it's accurate to within one percent of the TOTAL RANGE OF ERROR. The other 84% of the time, you're on your own. I wonder if I could manage that kind of accuracy just by sampling colors, classifying them by terrain, and then just picking a likely spot at random.
Yahoo! Pipes are awesome. How awesome? http://pipes.yahoo.com/jesdynf/slashdot
And has already been covered on /. over a year ago. Hell, it's been used to solve a crime in a popular police drama, so it is officially old news.
1/6 chance = 16.67% chance. Indeed, not very impressive for London.
Look at this guy's claim for basic audio analysis:
"Simply phonetics. The science of speech. That's my profession; also my hobby. Happy is the man who can make a living by his hobby! You can spot an Irishman or a Yorkshireman by his brogue. I can place any man within six miles. I can place him within two miles in London. Sometimes within two streets."
And that was almost a century ago!
Innovation makes enemies of all those who prospered under the old regime... -- Machiavelli
I propose we all take pictures with blue screen in them (not the whole background, just "enough") and then write a script to randomly replace the blue screen with alternative locations every time the picture loads.
I'm not with you in the argument. Assuming there are just 6 cities, and that the proportion from each is the same: 1/6, if you guess randomly you are right 1/6 of the time. It's just like a die... Then, if there are zillions of sources but only six cities amount for most of the pictures, then randomly guessing among them will get you close to this 1/6...
Dawkins Revisited: A person is shit's way of making more shit -- Steve Barnett, anthropologist.
Wikipedia to the rescue - http://en.wikipedia.org/wiki/London_Bridge - London Bridge is actually pretty interesting as well as the original one was demolished, the second one was moved to Arizona, and the third one currently standing in London was built between 1967 and 1972.
Reminds me of the experiment done in a Dutch military lab a couple of years ago. They trained a neural network to recognize whether a photograph taken out on a country road had a military vehicle in it or not.
The system recognized the photos from the training set perfectly, but did no better than random on images fed to it that were taken at different times.
Turns out all the training shots with a military vehicle in it had been taken on a sunny day, and the control shots without one had been taken when it was overcast. The system had been trained to recognize a different thing from what they intended!
There's an upcoming paper coming from MIT on this topic, Recognition of Natural Scenes from Global Properties: Seeing the Forest Without Representing the Trees that proves this isn't as hard as you might think.
To sum up this massive paper in a very small (and likely highly imprecise) nutshell, building models up from basic objects (the traditional method) is only one way to approach this. Using this method, you are correct; it's impossible to understand what a canyon is. Using the new global properties methods in this upcoming paper, you can gather basic elements that could easily help in assigning location properties, understanding that something pictured is a desert or forest, and theoretically using that data to help determine which desert or forest (this latter portion is beyond the scope of the paper, but great fodder for a future paper that builds upon these fundamentals).
While the method currently requires a high level of labeling in its images, it is hoped that this labeling becomes unnecessary on larger data sets.
Use my userscript to add story images to Slashdot. There's no going back.
I would know it was Waimea Canyon because I've been there, and to the Grand Canyon, and as others have said the amount of vegitation and steepness of the walls differentiate the two very clearly, and i know that canyons that large are rare (on earth, above water), so it is unlikely to be some other place i just haven't been. :)
But i know a lot more than computers.
This all brings up an interesting point though... When you're in an unfamiliar place, but your friend knows the area, they can always tell you where you are, more or less. When machines get good enough to have a complete knowledge of the entire earth down to a sufficient resolution, they WILL be able to look around, anywhere, and tell you where you are, without GPS.
Also, they will be able to direct you to the nearest human enslavement camp.
-Taylor
Worldwide Military budgets: $2100 billion. Worldwide Space Exploration budgets: $38 billion. Really, world? Really?
Google should get behind this. I think their Picasa would benefit from it.
Generate some autotags.
What would be nice also is if they had a feature where if you labeled someone in a picture, if you uploaded another picture with that person in the picture, the program would prompt to auto tag.
I've been going through old family photos and it would save so much time if the programs I am using autolabeled based off details in the picture.
"Only one thing, is impossible for god: to find any sense in any copyright law on the planet." Mark Twain
I'd imagine great work could be done by examining light intensity and coloration (atmospheric red shift) vs date stamp on the image (working from RAW with some camera data), they could guess the latitude fairly accurately. By similar methods you could figure out pollution levels, thus narrowing the sample range further.
Additionally comparing geometry could help factor out region with plant recognition fairly well also. You're not going to see a saguaro in Kentucky unless you're in a botanical garden. They've got a rather distinctive shape, and somewhat unique coloration.
Then you've got horizon lines - they're going to be ragged everywhere.
City skylines can be fairly easily identified the same way barcodes can be recognized, and mountain ridgelines are equally useful. The real trick would be telling a place in western Montana in mid-spring vs a place in western Kansas in early fall.
The land shall stone them with the bread of his son.