Approximations in the HMAX Model

The HMAX model is a biologically motivated architecture for computer vision whose components are in close agreement with existing physiological evidence. The model is capable of achieving close to human level performance on several rapid object recognition tasks. However, the model is computationally bound and has limited engineering applications in its current form. In this report, we present several approximations in order to increase the efficiency of the HMAX model. We outline approximations at several levels of the hierarchy and empirically evaluate the trade-offs between efficiency and accuracy. We also explore ways to quantify the representation capacity of the model.

[1]  Rainer Lienhart,et al.  Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection , 2003, DAGM-Symposium.

[2]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Thomas Serre,et al.  A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex , 2005 .

[4]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).