Neural Models for Part-Whole Hierarchies

Riesenhuber, M; Dayan, P

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Neural Models for Part-Whole Hierarchies

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https://papers.nips.cc/paper/1236-neural-models-for-part-whole-hierarchies.pdf
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Riesenhuber, M., & Dayan, P. (1997). Neural Models for Part-Whole Hierarchies. In M. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in Neural Information Processing Systems 9 (pp. 17-23). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/21.11116/0000-0002-E300-D

Abstract

We present a connectionist method for representing images that explicitly addresses their hierarchical nature. It blends data from neuroscience about whole-object viewpoint sensitive cells in inferotemporal cortex 7 and attentional basis-field modulation in V4 3 with ideas about hierarchical descriptions based on microfeatures. 4, 10 The resulting model makes critical use of pathways for both analysis and synthesis. 5 We illustrate the model with a simple example of representing information about faces. 1 Hierarchical Models Substantial recent effort has been devoted to analysis-by-synthesis models for visual processing. 15,5 The synthetic or generative models form the map: `object' ! `image' (1) where `object' implies the identity of the object (such as a face) and its relevant instantiation parameters (such as whether it is smiling or frowning) and `image' stands in for the pattern of activity over the input units that is caused by observing the object.