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!
... 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?
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"?)
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/