Bernal S D_2010.pdf - University of Plymouth

4.2. ARCHITECTURES
Scale band
RF size", AA'i-2
S2 types, Ksi
Band pooling, ^c2
Grid size, ANc2
Sampling, ec2
C2 types, Kfi
RF size^, ANsi
S3 types, Ksi
1
4
S2 parameters
2
3
8
12
1000
C2 parameters
All bands: 1 8
0.5 X total S2 units
0.25 X total S2 units
1000
S3 parameters
2
2x2x60 = 240
"83 protoiype elemeffls= ANs2 x ^f^si " f^C\ f* orienlalions). Same for all scale bands.
''S3 proiolype elt;ments= AWss x AN^i x Kci (1000 feaiurra).
Table 4.3: Parameters of the aliemativc .^-layer archlieciure based on Yamane ei al. (2006).
to Just 2 X 2 C2 units, and lo learn 2x2 prototypes (one (breach location) per object category,
which leads to greater position invariancc at the lop layer. As a consequence, the learned high-
level prototypes of objects contain some information about the spatial arrangement of Iheir
constituent pans, in agreement with the results shown by Yamanc ct al. (2006). This was also
discussed in Section 2.1.1 and illustrated in Figure 2.3b.
Additionally, the smaller pooling ranges of each unit help to reduce the large fan-in of A mes­
sages and to increase the speciticity of feedback. All feedback results presented in the thesis are
based on this architecture.
4.2.3 Four-level architecture
This architecture is based on the version of HMAX dcscril>ed in Scrre et al. (2(M)7b), and in­
cludes two extra layers. The parameters of this architeclure are shown in Table 4.4 and the
resulting Bayesian network is illustrated in Figure 4.6 (only layers above S2, as layers SI and
CI are equivalent those shown in Figure 4.4). The main advantage of this architecture is the
further processing by the two extra layers with smaller pooling ranges which leads to greater
position and scale invariance and can increase selectivity in highly detailed images. However,
these two extra layers also lead to greater complexity and higher approximation errors when
implementing the model as a Bayesian network. For this reason, the four-level architecture was
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