Class Similarity and Viewpoint Invariance in the Recognition of 3D Objects

Abstract

In human vision, the processes and the representations involved in identifying specific individuals are frequently assumed to be different from those used for basic-level classification, because classification is largely viewpoint-invariant, but identification is not. This assumption was tested in psychophysical experiments, in which objective similarity between stimuli (and, consequently, the level of their distinction) varied in a controlled fashion. Subjects were trained to discriminate between two classes of computer generated 3D objects, one resembling monkeys, and the other dogs. Both classes were defined by the same set of 56 parameters, which encoded sizes, shapes, and placement of the limbs, the ears, the snout, etc. Interpolation between parameter vectors of the class prototypes yielded shapes that changed smoothly between monkey and dog. Within-class variation was induced in each trial by randomly perturbing all the parameters. After the subjects reached 90% correct performance on a fixed canonical view of each object, discrimination performance was tested for novel views that differed by up to 60 deg from the training view. In experiment 1 (in which the distribution of parameters in each class was unimodal) and in experiment 2 (bimodal classes), the stimuli differed only parametrically and consisted of the same geons (parts), yet were recognized virtually independently of viewpoint in the low-similarity condition. In experiment 3, the prototypes differed in their arrangement of geons, yet the subjects' performance depended significantly on viewpoint in the high-similarity condition. In all three experiments, higher inter-stimulus similarity was associated with an increase in the mean error rate and, for misorientation of up to 45 deg, with an increase in the degree of viewpoint dependence. These results suggest that a geon-level difference between stimuli is neither strictly necessary nor always sufficient for viewpoint-invariant performance. Thus, basic and subordinate-level processes in visual recognition may be more closely related than previously thought.