Slashdot Mirror
New Algorithms Improve Image Search
bc90021 writes "Electrical engineers from UC San Diego are making progress on an image search engine that analyzes the images themselves. At the core of this Supervised Multiclass Labeling system is a set of simple yet powerful algorithms developed at UCSD. Once you train the system (the 'supervised' part), you can set it loose on a database of unlabeled images. The system calculates the probability that various objects it has been trained to recognize are present, and labels the images accordingly. After labeling, images can be retrieved via keyword searches. Accuracy of the UCSD system has outpaced that of other content-based image labeling and retrieval systems in the literature. One of the co-authors works at Google, where the researchers have access to image collections at the largest of scales."
I remember when we had to go to a gas station and *buy* porn. Now you have computers out there finding porn for you. You kids today have it too easy!
change the way I search for Natalie Portman p0rn?
Microsoft: "You've got questions. We've got dancing paperclips."
... was similarly trained to recognise tanks in landscapes. I was doing really well - getting a great score on the fresh images it was presented with.
Then they introduced it to a new batch of images and it fell apart.
Turns out that the initial set of images had all the tanks shot on a sunny day and all the tankless images shot on a cloudy day (or vice versa). It had learned to tell a sunny day from a cloudy day.
Ha ha.
I find it disturbing that you combine porn, your daughter, and rabbits all in your post.
You have issues.
Would you kindly mod me +1 insightful?
The problem is we all know what's gonna be the first result when searching "Caves on uranus"!!!
--
Great hosting 200GB Storage, 2_TB_ bandwidth, php, mysql, ssh, $7.95
Not if it is a Bayesian probability.
Run this story again when the system can tell the difference between D, DD, and DDD. Bonus points if it can handle "higher" criteria.
>
> Snarkiness aside, this is pretty cool stuff. I hope to see usable OSS code in a few years. Imagine how cool it would be to query "show me all pics with my daughter and her rabbits" and have it week through the 1000's of digital family photos.
But apart from the fact that it's almost Easter, what's with the rabbits? *clickity clic*-hey, I didn't know you could do that with Cadbury easter creme eggs!
(Rule #34: There is porn of it. No exceptions.)
No folly is more costly than the folly of intolerant idealism. - Winston Churchill
Since a huge % (perhaps most) image searches are for porn, it is probably a worthwhile thing for a search server to quickly classify likely porn as a way to reduce search server loading.
Engineering is the art of compromise.
Fortunately, this is Slashdot, so discussions of pr0n that don't feature square-waves, multipliers, and exponential backoff functions are apparently incomprehensible too!
(What are these "girls" of which you speak? I only remember Millie Amp... she was imaginary, skinny as a wire, but when her insulation got stripped, she stopped resisting, got really hot, and started to moan "ohm, ohm, ohm"?)
For instance, the set of pictures for which the statement "is this a picture of a chair" is true. There is no objective criteria for this. So imagine you have a bunch of pictures and show each one to a thousand people. Sometimes you might get 0 or 1000 "yes" responses, but often you'll get some number in between (because there are chairs, but barely visible, the picture includes a kids booster seat, or a rock big enough to sit on). This could be interpreted as a probability that somebody will consider a picture to be of a chair.
By 'girls', I mean the limiting reagent in human reproduction. As a class of compounds, 'girls' are extremely common but somewhat volatile, so creating bonds with them is sometimes difficult. They are attracted to other similarly elusive compounds. Examples of these attracting compounds include 'Time', 'emotional vulnerability', and 'financial stability'.
I find it interesting which ones of the object-recognition and scene categorization algorithms make it to Slashdot.
m
Why does this one make it?
This is a very hot research topic at the moment.
to name a couple of groups:
http://www.robots.ox.ac.uk/~vgg/
http://lear.inrialpes.fr/
http://www.vision.caltech.edu/
http://www.science.uva.nl/research/isla/
http://www.cdvp.dcu.ie/
http://www.informedia.cs.cmu.edu/
http://www.research.ibm.com/slam/
http://www.ee.columbia.edu/ln/dvmm/newResearch.ht
oh, and people should not stare themselves blind on the claimed results.
Research papers *always* have to present good results, or else you do not get published.
Furthermore, these images are of a very high quality, make by professional photographers.
Many algorithms perform very well on these ('corel'-like) sets, while utterly failing if applied on real-world data:
http://www-nlpir.nist.gov/projects/trecvid/