We do not merely
recognize objects, our brain is so good at this task that we can automatically
supply the concept of a cup when shown a photo of a curved handle or identify a
face from just an ear or nose. Neurobiologists, computer scientists, and robotics
engineers are all interested in understanding how such recognition works (in
both human and computer vision systems). New research suggests that there is an
atomic unit of recognition, a minimum amount of information an image must
contain for recognition to occur. In the field of computer vision, for example,
the ability to recognize an object in an image has been a challenge for
computer and artificial intelligence researchers. Researchers, wanted to know
how well current models of computer vision are able to reproduce the capacities
of the human brain. To this end they enlisted thousands of participants from
Amazon's Mechanical Turk and had them identify series of images. The images
came in several formats: Some were successively cut from larger images,
revealing less and less of the original. Others had successive reductions in
resolution, with accompanying reductions in detail.
When the
scientists compared the scores of the human subjects with those of the computer
models, they found that humans were much better at identifying partial- or
low-resolution images. The comparison suggested that the differences were also
qualitative: Almost all the human participants were successful at identifying
the objects in the various images, up to a fairly high loss of detail -- after
which, nearly everyone stumbled at the exact same point. The division was so sharp;
the scientists termed it a phase transition. The researchers suggest that the
differences between computer and human capabilities lie in the fact that
computer algorithms adopt a bottom-up approach that moves from simple features
to complex ones. Human brains, on the other hand, work in "bottom-up"
and "top-down" modes simultaneously, by comparing the elements in an
image to a sort of model stored in their memory banks. The findings also
suggest there may be something elemental in our brains that are tuned to work
with a minimal amount, a basic atom of information. That elemental quantity may
be crucial to our recognition abilities, and incorporating it into current
models could improve their sensitivity.
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