On the Role of Shape Prototypes in Hierarchical Models of Vision

We investigate the role of learned shape-prototypes in an influential family of hierarchical neural-network models of vision. Central to these networks’ design is a dictionary of learned shapes, which are meant to respond to discriminative visual patterns in the input. While higher-level features based on such learned prototypes have been cited as key for viewpoint-invariant object-recognition in these models [1], [2], we show that high performance on invariant object-recognition tasks can be obtained by using a simple set of unlearned, “shape-free” features. This behavior is robust to the size of the network. These results call into question the roles of learning and shape-specificity in the success of such models on difficult vision tasks, and suggest that randomly constructed prototypes may provide a useful “universal” dictionary.