@article {1760,
	title = {Atoms of recognition in human and computer vision},
	journal = {PNAS },
	volume = {113},
	year = {2016},
	month = {03/2016},
	pages = {2744{\textendash}2749},
	abstract = {<div>Discovering the visual features and representations used by thebrain 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 remarkableprogress and have begun to rival human performance in some challenging tasks. These models are trained on image examples andlearn 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 aresimilar to those used by the human visual system. Here we show,by introducing and using minimal recognizable images, that thehuman visual system uses features and processes that are not usedby current models and that are critical for recognition. We found bypsychophysical studies that at the level of minimal recognizableimages a minute change in the image can have a drastic effect onrecognition, thus identifying features that are critical for the task.Simulations then showed that current models cannot explain thissensitivity to precise feature configurations and, more generally,do not learn to recognize minimal images at a human level. The roleof 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 ourunderstanding of visual recognition and its cortical mechanisms andwill enhance the capacity of computational models to learn fromvisual experience and to deal with recognition and detailedimage interpretation.</div>
},
	keywords = {Computer vision, minimal images, object recognition, visual perception, visual representations},
	issn = {1091-6490},
	doi = {10.1073/pnas.1513198113},
	url = {http://www.pnas.org/content/113/10/2744.abstract},
	author = {Shimon Ullman and Liav Assif and Eitan Fetaya and Daniel Harari}
}
