Bernal S D_2010.pdf - University of Plymouth

S.i. FEEDFORWARD PnOCESSING
5.1.2.4 Comparison of difTerent models
Figure 5.17 compares the categorization pert'onnance of the three versions of the model pro­
posed, namely the 3-leve! architecture, the 4-level architecture and the allemalive 3-lcvel archi­
tecture, the HMAX model and an HTM network. 1-or the 4-level architecture only the results
for the normal daiasei were calculated, as its poor performance suggested the results on the
transformed datasets would be extremely low and dius not worth the computational cost.
The HMAX-like model was implemented using Matlab and repticales Ihe model described in
Serre et ai. (2007c). i.e. the 3-level HMAX iniplenienlalion. Following the original HMAX
implementation, the S2 prototypes are selected at random from the training set, as opposed to
employing the minimum-dislanre algorithm implemented in Ihe Bayseian network model (see
Section 4.3). The HTM-like results were obtained using the Numenifi Vision Toolkit (George
and Hawkins 2009), which allows one to train and test an HTM network. However, only 50
categories are allowed, so 10 categories had to be eliminated from the training and testing
datasets.
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