New Object Recognition Algorithm Learns On the Fly
Zothecula writes "Scientists at Brigham Young University (BYU) have developed an algorithm that can accurately identify objects in images or videos and can learn to recognize new objects on its own. Although other object recognition systems exist, the Evolution-Constructed Features algorithm is notable in that it decides for itself what features of an object are significant for identifying the object and is able to learn new objects without human intervention."
...but I don't think an evolutionary algorithm approach to pattern recognition is anything new.
Let it loose at the Adult Entertainment Expo.
If it can figure out what half of that stuff is, it's a brilliant algorithm.
If not, it will probably be hilarious to see the results.
Lost at C:>. Found at C.
I know it's popular for people to immediately start with all the Terminator-claims and whatnot, but that's not the first thing that comes to my mind when reading stuff like this. Personally, I think of coupling this with something like e.g. Google Glass, so that you can tell the system to identify the item in the center of the view and then ask for it to automatically search for instructions on use or repair or whatnot. Even better if you have a device that covers both of your eyes so that the system can overlay things in your whole visual field, identifying things and showing their connections and whatnot.
At least there's one professor at BYU that believes in evolution!
Does it work in real time? I can't find any more information than marketing buzz in the article (and the BYU article)...
Is there a paper or anything with a bit more [technical] detail?
I would hope they'd get a sensor that would be able to identify new objects. Maybe let a robot pick it up, spin it around, get a 3d digitization model of the object then label it something temporary until someone tells it what it is. A quick look at how AI is probably going to be done when all the techs come together
God spoke to me
Anyone got a link to the actual paper?
I wonder if this can be used for image compression. Because if you know e.g. what a bicycle looks like, you don't have to compress it.
If Pandora's box is destined to be opened, *I* want to be the one to open it.
Nothing is performed on the fly. It's just another feature extraction and selection pipeline.
1) Deep Neural Networks also save the feature engineering step (for instance http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/1210.pdf)
2) If as suggested by the title you are interested by on-the-fly object recognition, look at Tracking-Learning-Detection (TLD) (http://info.ee.surrey.ac.uk/Personal/Z.Kalal/tld.html)
Hope it will not fail "Tranny or Female" test
New object recognition algorithm learns on the fly
I know wearable computing is the next big thing but putting one there - especially if it has a camera attached - is going to look a little bit... weird.
systemd is Roko's Basilisk.
notable in that it decides for itself what features of an object are significant for identifying the object and is able to learn new objects without human intervention
For Christ's sake. The AdaBoost face-detection algorithm - the one that everyone uses today - does precisely this, and was developed in the the 90's.
Why should I get excited about something written by a journalist where there must be something writeen by scientists? Where is the PDF of the scientific paper to download?
First of all, is this the right paper?
It seems that the topic of the linked article is a new unsupervised algorithm that categorizes images. The linked article says that 'the Evolution-Constructed Features algorithm is notable in that it decides for itself what features of an object are significant for identifying the object', which unsupervised algorithms do implicitly, no? It is also stated that the algorithm 'is able to learn new objects without human intervention' - so if I'm interpreting this and the article's abstract correctly, the algorithm uses a novel approach to coding some sort of more or less raw image data which it receives as input? Otherwise, it appears that what makes the approach newsworthy is its extremely high accuracy, which was 95 to 100% on some measures. That sounds very good if the tests were representative of a real-world environment.
team,
fyi: http://contentdm.lib.byu.edu/utils/getfile/collection/ETD/id/3021/filename/503.pdf
-me
Perhaps if this was explained in terms of an example whereby you describe how existing AI uses or learns the training set and how the newfangled way does it different.
Table-ized A.I.
This sounds a lot like the Never Ending Image Learner project: http://www.neil-kb.com/ which is crawling the web and trying to extract visual knowledge.
Looks like a bit of click-bait sensationalism by Gizmag. This algorithm is a couple of years old, the new research is just related to a new paper on domain specific usage (classifying fish). It's an unsupervised genetic algorithm, that uses basic image processing steps as the genes, hence Gizmag trying to tout it as 'learning on its own'. It's a cool technique outright, but not as world changing as they make out.