Three demos illustrate the biologically inspired grayscale/Single-Opponent(SO)/Double-Opponent(DO) HMAX model for object recognition

HMAX model was successfully used in real data, see details in: Serre, T., Wolf, L., Bileschi, S.M., Riesenhuber, M., Poggio, T. Robust object recognition with cortex-like mechanisms.TPAMI, 2007.

demoRelease : grayscale Hmax demoSoRelease: SOHmax (For most cases, two orientations of SO is sufficient due to the weekly oriented property) demoDoRelease: DOHmax

dataset: soccer team dataset (color-predominant) The dataset consists of 280 images falling into 7 classes, and was originally introduced in: van de Weijer, J., Schmid, C. Coloring local feature extraction. In: ECCV, 2006.

c1 prototypes: randomly extracted from 250 patches of 4 patch sizes (1000 patches in total)

We provided three types of C1 patches: dict_250_patches_4_sizes : grayscale dictSo_250_patches_4_sizes: SO dictDo_250_patches_4_sizes: DO

If you use it, please cite: Zhang J., Barhomi Y., and Serre T. A new biologically inspired color image descriptor.In: ECCV, Florence, Italy, October 2012.

For comments or questions please contact Jun Zhang ([email protected])