Teaching computers to see -- by learning to see like computers

They comb through databases of previously labeled images and look for combinations of visual features that seem to correlate with particular objects. Then, when presented with a new image, they try to determine whether it contains one of the previously identified combinations of features. Even the best object-recognition systems, however, succeed only around 30 or 40 percent of the time -- and their failures can be totally mystifying. Researchers are divided in their explanations: Are the learning algorithms themselves to blame? Or are they being applied to the wrong types of features?