The human visual cortex has evolved to determine efficiently objects from within a scene. Hierarchical MAX (HMAX) is an object recognition model which has been inspired by the visual cortex, and sparse coding, which is a characteristic of neurons in the visual cortex, was previously integrated into the HMAX model for improved performance. In this study, in order to further enhance recognition accuracy, we have developed an elastic net-regularized dictionary learning approach for use in the HMAX model. We term this the En-HMAX model. With the En-HMAX model, we can exploit the sparsity-grouping tradeoff, such that correlated but informative features are preserved for object classification. Results show that the En-MAX model outperforms the original HMAX model in recognizing unseen objects by   $\sim$  40   $\%$   as well as the two special cases of the HMAX model, i.e., the least absolute shrinkage and selection operator (LASSO)-HMAX (  $\sim$   19  $\%$  ) and Ridge-HMAX (  $\sim$  9   $\%$  ) models.
['Ali Alameer', 'Ghazal Ghazaei', 'Patrick Degenaar', 'Jonathon A. Chambers', 'Kianoush Nazarpour']
Object Recognition With an Elastic Net-Regularized Hierarchical MAX Model of the Visual Cortex