  In this paper, an efficient implementation for a recognition system based on
the original HMAX model of the visual cortex is proposed. Various optimizations
targeted to increase accuracy at the so-called layers S1, C1, and S2 of the
HMAX model are proposed. At layer S1, all unimportant information such as
illumination and expression variations are eliminated from the images. Each
image is then convolved with 64 separable Gabor filters in the spatial domain.
At layer C1, the minimum scales values are exploited to be embedded into the
maximum ones using the additive embedding space. At layer S2, the prototypes
are generated in a more efficient way using Partitioning Around Medoid (PAM)
clustering algorithm. The impact of these optimizations in terms of accuracy
and computational complexity was evaluated on the Caltech101 database, and
compared with the baseline performance using support vector machine (SVM) and
nearest neighbor (NN) classifiers. The results show that our model provides
significant improvement in accuracy at the S1 layer by more than 10% where the
computational complexity is also reduced. The accuracy is slightly increased
for both approximations at the C1 and S2 layers.
