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Automatic Image Tagging
Posted by CowboyNeal on Thursday November 02, @08:07PM
from the on-the-horizon dept.
from the on-the-horizon dept.
bignickel writes "Researchers at Penn State have applied for a patent on software that automatically recognizes objects in photos and tags them accordingly. The 'Automatic Linguistic Indexing of Pictures Real-Time' software (catchy name) trained a database using tens of thousands of images, and new images have 15 tags suggested based on comparisons with objects or concepts in the database. Not sure how you identify a 'concept,' and they're only talking about having one correct tag in the top 15, but still cool."
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Not shockingly...(Score:5, Funny)
That Sucks(Score:2, Funny)
(http://www.behti.com/ | Last Journal: Monday July 25, @03:30AM)
The other 50% is the problem(Score:3, Informative)
I've seen lots of systems like this. The problem is in the 50% of the images that don't work, so basically you have to manually tag 50% of your images.
I saw an interesting one about 10 years ago. It took an X-Ray image, did an edge detection, converted all the edges to a slope vs distance 2D plot, and conerted edge curves to a radius and distance plot, then used a kind of statistical correlation algorithm to pick which part of the body the image was from. I could imagine that you could apply something similar to the luminance of an image to pick out objects, and then maybe do some color transforms and stuff to improve results. The article says they do it in 1.4 seconds per image though, which is impressive.
(http://www.sirch.co.uk/ | Last Journal: Friday September 19, @04:30PM)
We didn't have enough time to train the system properly, but itstarted off well...
Google, others doing similar research?(Score:2)
A video an the subject(Score:2, Informative)
View the video on Human Computation [google.com]
Nobody is impressed by this(Score:1)
I swear I saw a butterfly(Score:2)
1 out of 15 ? impressive(Score:2)
(yes, assuming a normal distribution of 'concepts' in the pictures, etc)
(http://www.redorbit.com/ | Last Journal: Monday October 23, @03:21PM)
That's cool, the rest of it will be like opening xmas presents!
*file: 123456.jpeg>open>Aghh! Goatse!*
Hmmm...This may be neat when it gets a LITTLE more accurate, but a cool start none the less.
Kudus to the gang for getting a grip on a hard problem...erm..nevermind.
In future news...(Score:1)
IBM was working on this years ago...(Score:2)
Not sure how far they got, but remember reading that IBM was working on this and had some reasonable success at object recognition in images. I'd love to be able to classify the 10k digital images I've got around. Especially if it can recognize individuals (not that it would know their names initially, but would be trainable).
The system has clearly been let crawl the web for far too long.
Google will buy it...(Score:1)
Unless Jupiter Media [jupitermedia.com] gets to it first.
Someone like myself would understand the hours of data-entry and database development that goes into indexing imagery. I research photo copyrights for a living.
The fact that there is a feasible, automated system that can do the work will certainly cut down the man-hours for that sort of work; at least by half.
Pity, though. I heard that Google and others had a telecommuting thing that paid people to recognize what's in a photo. Sorry to hear they'll be out of a job soon.
Seriously now, I am sure their are people out there that have already got ideas rolling around in their heads about how they can use this technology to hijack images to their advantage. Once somebody understands how the technology works it is only a matter of time before it is used for nefarious purposes, by means of "tricking" the technology. And in the process, invalidating any possible means by which the developers can realize a return on their investments.
Personally, I'd love to use such a technology(if it actually works) to sift through the plethora of "crap" images I have to search through on the web. It can be really frustrating to do a search only to find that a vast amount of the results are TOTALLY out of context simply because of the title tag attached.
One tag in 15 correct(Score:1)
image spam filtering(Score:2)
... usually a pedant... but you don't train a database. It was likely a neural net, but TFA is rather thin on details. Anyone got a link to their paper?
Lots of implications for Surveillance, however.(Score:1, Interesting)
Is there a "Big Brother" category on Slashdot, yet?
Well I sure hope they invented it before early 80s(Score:1)
So they say...(Score:2)
(http://www.ninwa.net/ | Last Journal: Thursday July 27, @06:55PM)
(Last Journal: Saturday November 30, @01:53AM)
(Last Journal: Friday September 17, @04:10PM)
And that's one of the problems: does an image define the taxonomy or taxonomy defines the image [type]?
When it's going to be on Flickr?(Score:1)
(http://licensetransfer.wordpress.com/ | Last Journal: Thursday August 12, @03:32AM)
Neural Nets(Score:2, Insightful)
That's like patenting training a dog to fetch a stick, it's completely rediculous.
You take software capable of generalizing a neural network algorithm by feeding it pictures and associating each picture with certain tags. It then creates a generalized algorithm model based on what you fed it initially. So that when you give new input it is capable of outputting tags most similar to what you initially trained it.
So yes this software can recognize boxes, shapes, other objects, maybe scenes etc and associate them with tags... but ask them how the algorithm works under the hood =) They have no idea... a neural network is like a black box after it has been trained. You feed it input and it gives you output based on it's initial training. The inner workings are chaotic spaghetti values set on each neuron weighting and can't be deciphered.
How can you patent software that is a black box inside?
"Yes hello patent office? I have this box that manufactures microprocessors. I feed it all the materials and it outputs a shiny new processor. I am not sure of the manufacturing process internally but the output works great. I would like to patent this manufacturing process.
"Okay your patent number is 247286-"BLACK BOX"-9
The whole point of a neural network is it generalizes what you train it and can future predict any input based on that.
It's like having the invention of the first mirror and everytime someone put something different infront of it, that person called up the art gallery because they had a new painting that they wanted in their name (because depending what was in front of it you get a different reflection).
There's a few subjects that are so common that it's more or less a given they'll be in a large fraction of the photos. Outputting "people, buildings, nature, animals, plants, city" would probably give atleast 1-2 "correct" tags for 90% of whats in peoples photoalbums.
I had a class on neural networks and their (weak) sort of "ai", one task was to build a program to separate male from female names. The best programs could manage 80% or so, which is sorta decent. Until you realize that checking against static lists of the top 100 male/female names, if it's not in the list guess female if it ends in 'a', otherwise guess randomly will get you aproximately 95%. Furthermore, the latter program runs an order of magnitude faster, is more easily debuggable, can be understood by anyone, and can trivially be "extended" to reach 99% or more, simply by extending the lists of known male/female names.
(Last Journal: Monday December 12, @01:08PM)
Move along to real research.
wonder how this compares(Score:2)
I haven't RTFA and I don't have any experience with Riya either, so consider the above posting a waste of time (if you must).
Wouldn't it be easier...(Score:2)
Re:Tag for /.(Score:2)
Re:Just how broad is a concept?(Score:1)