On the optimization of Hierarchical Temporal Memory

In this paper an optimized classification method for object recognition is presented. The proposed method is based on the Hierarchical Temporal Memory (HTM), which stems from the memory prediction theory of the human brain. As in HTM, this method comprises a tree structure of connected computational nodes, whilst utilizing different rules to memorize objects appearing in various orientations. These rules involve both the spatial and the temporal module. As HTM is inspired from brain activity, its input should also comply with the human vision system. Thus, for the representation of the input images the logpolar was given preference to the Cartesian one. As compared to the original HTM method, experimental results exhibit performance enhancements with this approach, in recognition and categorization applications. Results obtained prove that the proposed method is more accurate and faster in training, whilst retaining the network robustness in multiple orientation variations.

► Hierarchical Temporal Memory. ► Correlation matrix. ► Greater classification rate than SVM.