"Hierarchical Models of Object Recognition in Cortex,"
Nature Neurosciece, Vol. 2, No. 11, November 1999. pp 1019-1025.
They say simple cells and complex cells and
complex cells are responsible for invariant properties.
Invariance can be implemented as a pooling mechanism
where there are view-invariant units and view-tuned units.
Hierarchical feedforward network is considered and
the network is based on MAX rather than linear summation (SUM).
MAX is proved to be more robust and invariant than SUM.
That is, Hierarchical MAX has view-invariant object recognition ability
and is supported by biological (physiological) facts.
Anyway, I say, this is a very similar concept to ISA, but a little more flexible.
But this paper doesn't say much of implementation such as
how to get the simple cells or complex cells,
and how to construct the network structure.
See [1] for a specific implementation and examples of HMAX.
In [1], interestingly, simple and complex cells are not learned from data
but designed by second derivative of Gaussians.
[1] T. Serre and M. Riesenhuber,
"Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model,
and Implications for Invariant Object Recognition in Cortex,"
AI Memo 2004-017, CBCL Memo 239, MIT, July 2004.
- H. Choi
