B-HMAX: A fast binary biologically inspired model for object recognition

The biologically inspired model, Hierarchical Model and X (HMAX), has excellent performance in object categorization. It consists of four layers of computational units based on the mechanisms of the visual cortex. However, the random patch selection method in HMAX often leads to mismatch due to the extraction of redundant information, and the computational cost of recognition is expensive because of the Euclidean distance calculations for similarity in the third layer, S2. To solve these limitations, we propose a fast binary-based HMAX model (B-HMAX). In the proposed method, we detect corner-based interest points after the second layer, C1, to extract few features with better distinctiveness, use binary strings to describe the image patches extracted around detected corners, then use the Hamming distance for matching between two patches in the third layer, S2, which is much faster than Euclidean distance calculations. The experimental results demonstrate that our proposed B-HMAX model can significantly reduce the total process time by almost 80% for an image, while keeping the accuracy performance competitive with the standard HMAX.