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An Advance In Image Recognition Software
Roland Piquepaille alerts us to work by US and Israeli researchers who have developed software that can identify the subject of an image characterized using only 256 to 1024 bits of data. The researchers said this "could lead to great advances in the automated identification of online images and, ultimately, provide a basis for computers to see like humans do." As an example, they've picked up about 13 million images from the Web and stored them in a searchable database of just 600 MB, making it possible to search for similar pictures through millions of images in less than a second on a typical PC. The lead researcher, MIT's Antonio Torralba, will be presenting the research next month at a conference on Computer Vision and Pattern Recognition.
This will be used to identify YOU, citizen.
"Flyin' in just a sweet place,
Never been known to fail..."
"They're going to distinguish an individual based on images with 256 to 1024 bits of data?"
No one said they were going to identify individual people with this. The main gist of this research seems to be efficiency (in both space and time, if I read it correctly). For instance, if one wanted to identify every face in a picture of a crowd, they could apply this algorithm to a low-res version of the image to quickly find the locations of every "face," and then use a more advanced face recognition algorithm to actually figure out who it is they're looking at.
Jeff Hinton worked with them, you really think they're plagiarizing him? That claim doesn't even make sense, this is a novel research domain. A big part of science is taking people's ideas, reproducing them, and applying them to novel domains. That's how it's SUPPOSED to work.
This research involves the use of one of the largest image databases seen in computer vision. It shows that you can do extremely rapid scene matching for databases of this scale. No, that's not obvious no matter what you think. This image data is fairly high dimensional.
This research says something about the space of likely scenes and it might be a key enabling technology to a lot of the heavily data driven computer vision and computer graphics approaches popping up lately.