There is an ongoing debate about the nature of perceptual representation in human object recognition. Resolution of this debate has been hampered by the lack of a metric for assessing the representational requirements of a recognition task. To recognize a member of a given set of 3-D objects, how much detail must the objects' representations contain in order to achieve a specific accuracy criterion? From the performance of an ideal observer, we derived a quantity called the view complexity (VX) to measure the required granularity of representation. VX is an intrinsic property of the object-recognition task, taking into account both the object ensemble and the type of decision required of an observer. It does not depend on the visual representation or processing used by the observer. VX can be interpreted as the number of randomly selected 2-D images needed to represent the decision boundaries in the image space of a 3-D object-recognition task. A low VX means the task is inherently more viewpoint invariant and a high VX means it is inhernltly more viewpoint dependent. By measuring the VX of recognition tasks with different object sets, we show that the current confusion about the nature of human perceptual representation is partly due to a failure in distinguishing between human visual processing and the properties of a task and its stimuli. We find general correspondence between the VX of a recognition task and the published human data on viewpoint dependence. Exceptions in this relationship motivated us to propose the view-rate hypothesis: human visual performance is limited by the equivalent number of 2-D image views that can be processed per unit time.
|Original language||English (US)|
|Number of pages||16|
|State||Published - Aug 1998|
Bibliographical noteFunding Information:
This work was supported by NIH Grant EY02857 to Gordon E. Legge. An earlier report appeared as a chapter in Bosco S. Tjan’s doctoral thesis  . We thank Zili Liu for his helpful comments, Jeff Liter for suggestions on statistics and Bernhard Schölkopf for discussions on statistical learning theory.
- Ideal observer
- Object recognition
- Perceptual representation
- Viewpoint effects