Bernal S D_2010.pdf - University of Plymouth

4.3. LEARNING
S\ii node. Therefore, ihe origintil weighl niiilrices are convened inio one CPT per SI node as
shown al the bottom left of Figure 4.7.
The implementation in the real model follows that described in the loy example. The method
employed lo learn Ihe Kc\group mosi common arrangemcnls of SI units at each orientation feed­
ing to a CI unit is a clustering technique, namely the k-means algorithm (Bishop 1995). This
method can be understood as a type of Expectation-Maximizalion algorithm. This clustering
method is applied over a set of SI patches obtained from the training input images. To select
and pre-process the set of patches to be clustered for the / SI state (orientation) or C1 group,
the following steps are performed:
1. Select the next patch, Pp„„;,r,at of size n = ANc\ •ANr\ -2 (bands) from each state of the
SI nodes' A response, i.e. ^(51(^1,,.] - '),vjheTe {b,j:._v} represent the band and location
of the S1 node and match the patch size, n; and / h a specific SI orientation or CI group.
2. Keep patch Ppomuiai on'y i' iis maximum value is above a threshold 7;,„„, where T„,in is
given as a function of the maximum overall value of X{S\b_i.v - ') Vi,A',y. Formally, if
maxiPpate^iai) > T,„i„ ihcn l\,.iecied = Ppoicnimi- "^^CK T„„„ = a - maT(A (i'l,,,,-^,. = ()), and
a typical value for a would be 0.9. This ensures that the weight matrix is calculated based
on significant A (SI) responses which are close to the overall maximum response elicited
by SI nodes, and not based just on the local maximum of each patch.
3. For each selected patch, PwUi-iedt keep only the values above Tmin and set all other values
to 0. i.e. Pseiirii'd - fl