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Atoms of recognition in human and computer vision
#MMPMID26884200
Ullman S
; Assif L
; Fetaya E
; Harari D
Proc Natl Acad Sci U S A
2016[Mar]; 113
(10
): 2744-9
PMID26884200
show ga
Discovering the visual features and representations used by the brain to
recognize objects is a central problem in the study of vision. Recently, neural
network models of visual object recognition, including biological and deep
network models, have shown remarkable progress and have begun to rival human
performance in some challenging tasks. These models are trained on image examples
and learn to extract features and representations and to use them for
categorization. It remains unclear, however, whether the representations and
learning processes discovered by current models are similar to those used by the
human visual system. Here we show, by introducing and using minimal recognizable
images, that the human visual system uses features and processes that are not
used by current models and that are critical for recognition. We found by
psychophysical studies that at the level of minimal recognizable images a minute
change in the image can have a drastic effect on recognition, thus identifying
features that are critical for the task. Simulations then showed that current
models cannot explain this sensitivity to precise feature configurations and,
more generally, do not learn to recognize minimal images at a human level. The
role of the features shown here is revealed uniquely at the minimal level, where
the contribution of each feature is essential. A full understanding of the
learning and use of such features will extend our understanding of visual
recognition and its cortical mechanisms and will enhance the capacity of
computational models to learn from visual experience and to deal with recognition
and detailed image interpretation.
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