HMeanMax: Placing HMAX and HoG into a unified framework

Yan Zhang, Qixia Jiang, Maosong Sun

Abstract: Recently, the bio-inspired model HMAX has attracted much attention for its amazing biological inspired structure and comparable performance to the state-of-the-art computer vision algorithms. The success of HMAX leads to our further exploration on it especially the connection between the biological mechanism and the engineering validity. By detailedly analyzing HMAX and a totally engineering-driven approach HoG, we find such two methods have similar structures excepts the different pooling strategies, max versus mean, thus can be placed into a unified framework. Therefore, we present a unified framework named HMeanMax to integrate HMAX and HoG via combining multiple types of pooling into a single hierarchical feature extractor. All the experimental results support our findings.