title: Understanding rapid category detection via multiply degraded images.
abstract: Rapid category detection, as discovered by S. Thorpe, D. Fize, and C. Marlot (1996), demonstrated that the human visual system can detect object categories in natural images in as little as 150 ms. To gain insight into this phenomenon and to determine its relevance to naturally occurring conditions, we degrade the stimulus set along various image dimensions and investigate the effects on perception. To investigate how well modern-day computer vision algorithms cope with degradations, we conduct an analog of this same experiment with state-of-the-art object recognition algorithms. We discover that rapid category detection in humans is quite robust to naturally occurring degradations and is mediated by a non-linear interaction of visual features. In contrast, modern-day object recognition algorithms are not as robust.
authors.affiliation: University of California, Berkeley, Berkeley, CA 94720, USA. chetan@berkeley.edu
authors.forename: Chetan
authors.initials: C
authors.lastname: Nandakumar
authors.forename: Jitendra
authors.initials: J
authors.lastname: Malik
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual / Q000502:physiology; D010775:Photic Stimulation / Q000379:methods*; D011930:Reaction Time; D012438:Saccades; D013997:Time Factors