Similarity to reference shapes as a basis for shaperepresentationShimon

We present a uniied approach to visual representation, addressing both the needs of superordinate and basic-level categorization and of identiication of speciic instances of familiar categories. According to the proposed theory, a shape is represented by its similarity to a number of reference shapes, measured in a high-dimensional space of elementary features. This amounts to embedding the stimulus in a low-dimensional proximal shape space. That space turns out to support representation of distal shape similarities which is veridical in the sense of Shepard's (1968) notion of second-order iso-morphism (i.e., correspondence between distal and proximal similarities among shapes, rather than between distal shapes and their proximal representations). Furthermore, a general expression for similarity between two stimuli, based on comparisons to reference shapes, can be used to derive models of perceived similarity ranging from continuous , symmetric, and hierarchical, as in the multidimensional scaling models (Shepard, 1980), to discrete and non-hierarchical, as in the general contrast models (Tversky, 1977; Shepard and Arabie, 1979).

[1]  Roger N. Shepard,et al.  Additive clustering: Representation of similarities as combinations of discrete overlapping properties. , 1979 .

[2]  N. Logothetis,et al.  Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.

[3]  Shimon Edelman,et al.  Receptive field spaces and class-based generalization from a single view in face recognition , 1995 .

[4]  H. Putnam Representation and Reality , 1993 .

[5]  J. O'Regan,et al.  Solving the "real" mysteries of visual perception: the world as an outside memory. , 1992, Canadian journal of psychology.

[6]  J. W. Hutchinson,et al.  Nearest neighbor analysis of psychological spaces. , 1986 .

[7]  M. Martin White Queen Psychology and Other Essays for Alice , 1995 .

[8]  R. Nosofsky Exemplar-Based Accounts of Relations Between Classification, Recognition, and Typicality , 1988 .

[9]  A. Tversky Features of Similarity , 1977 .

[10]  P. Suppes,et al.  REPRESENTATIONS AND MODELS IN PSYCHOLOGY , 1994 .

[11]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[12]  David Mumford,et al.  Mathematical theories of shape: do they model perception? , 1991, Optics & Photonics.

[13]  T Poggio,et al.  Fast perceptual learning in visual hyperacuity. , 1991, Science.

[14]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[15]  S. Ullman Aligning pictorial descriptions: An approach to object recognition , 1989, Cognition.

[16]  RepresentationSharon Duvdevani-Bar,et al.  On Similarity to Prototypes in 3 D Object , 1995 .

[17]  S. Edelman,et al.  Explorations of Shape Space , 1995 .

[18]  K Tanaka,et al.  Neuronal mechanisms of object recognition. , 1993, Science.

[19]  Shimon Edelman,et al.  Learning to Recognize Faces from Examples , 1992, ECCV.

[20]  R N Shepard,et al.  Multidimensional Scaling, Tree-Fitting, and Clustering , 1980, Science.

[21]  S. Edelman Representation of Similarity in 3D Object Discrimination , 1995 .