- Link:
- http://hdl.handle.net/1721.1/7178
- Collection:
-
- Subject
- AI
- Creators:
- Riesenhuber, Maximilian Schneider, Robert
- Format
- 12 p.
- Format
- 2137337 bytes
- Format
- 1062341 bytes
- Format
- application/postscript
- Format
- application/pdf
- Language
- en_US
- Relation
- AIM-2002-011
- Relation
- CBCL-218
- Description
- The HMAX model has recently been proposed by
Riesenhuber & Poggio as a hierarchical model of position- and
size-invariant object recognition in visual cortex. It has also
turned out to model successfully a number of other properties of
the ventral visual stream (the visual pathway thought to be crucial
for object recognition in cortex), and particularly of (view-tuned)
neurons in macaque inferotemporal cortex, the brain area at the top
of the ventral stream. The original modeling study only used
``paperclip'' stimuli, as in the corresponding physiology
experiment, and did not explore systematically how model units'
invariance properties depended on model parameters. In this study,
we aimed at a deeper understanding of the inner workings of HMAX
and its performance for various parameter settings and ``natural''
stimulus classes. We examined HMAX responses for different stimulus
sizes and positions systematically and found a dependence of model
units' responses on stimulus position for which a quantitative
description is offered. Interestingly, we find that scale
invariance properties of hierarchical neural models are not
independent of stimulus class, as opposed to translation
invariance, even though both are affine transformations within the
image plane.
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