Reviews: Object Recognition in Man, Monkey, and Machine, Representation and Recognition in Vision

This reprint of a special issue of Cognition edited by Tarr and Bu« lthoff is timely; for one thing, the year it appeared saw also the publication of another special issue devoted to the same topic, in Vision Research. These days, even a cursory survey of the periodicals where papers on object recognition are to be found (including psychology, computer science, and neuroscience publications) brings up hundreds of references. (1) It is likely, therefore, that a mere half a dozen chapters can only just awaken a sleeping reader (that is, a nonspecialist in object recognition, which would mean most readers if, as one hopes, this collection sells well). The question is, will this book also fill the reader with the resolve to find out more? My answer is, by and large, yes. I concur with Tarr and Bu« lthoff's view, expressed in the introduction, that progress in visual object recognition research in the past two decades has been `tremendous'. A reader just awakening from happy dreams of a comprehensive and definitive theory (a notion one cannot help associating with Marr, given the way his writings came to be interpreted in cognitive sciences) is certainly in for a jolt. Interestingly, the awakened dreamer will discover that the issue of object recognition has not been settled, not even in an unanticipated manner; rather, it is very much open. This observation is the focus of the 20-page introduction by the editors, who point out the need to reconcile conflicting theories, while making sense of a plethora of data, most of which were not available twenty years ago. Of the theories, there used to be, basically, one, the `structural descriptions' or recognition by components (Biederman 1987). Now there are, indisputably, several: alternatives such as the `view-based' approach have come of age, and can no longer be ignored. Although the introduction generally does a good job, Tarr and Bu« lthoff are less than careful in discussing the computational aspects of some of the theories. For example, on page 10 they claim that view normalisation models (Ullman 1989) `̀ must establish the direction of rotation before executing a rotation or alignment. Determining this information seems to imply that recognition, at least at a coarse level, has already occurred. '' The same fallacious argument, based on a misunderstanding of the alignment algorithm,(2) is repeated by Perrett et al on page 138, who credit it to Corballis. To my mind, the most exciting part of the introduction (and of the entire collection) is the attempt to synthesise a `middle path' approach to object representation (pages 10 ^ 15), aiming at a compromise between empirically problematic (Edelman 1997) `structural description' theories on the one hand, and inherently limited (Hummel 1999) holistic image-based theories on the other hand. Unfortunately, here too the computational issues are not treated as carefully as they should. For example, the discussion on page 15 mentions an `̀ appealing aspect of compositionality'' as if the latter were a solution for the shortcomings of the image-based models. Now, compositionality is a property that some say is required for efficient and open-ended representation (formally, a representation of a whole is compositional if it is derived by a set of symbol manipulation rules from the representations of its parts). While a representation that is compositional should indeed be good at representing structure, the converse is not true: structure can be represented by noncompositional means (van Gelder 1990; Edelman et al 2000). By glossing over this point and treating compositionality as a solution rather than the problem that it is, Tarr and Bu« lthoff weaken their own case against structural-description theories. Reviews Perception, 2000, volume 29, pages 501 ^ 504