Clustering and use of spatial and frequency information in a biologically inspired approach to image classification

In this paper, we explore the use of spatial and frequency information of features in the biologically inspired model of HMAX. We discuss and refine previous models which use a similar framework and build specialized features which are better tuned to image structures by using unsupervised methods of clustering and picking the most frequent features using the statistics of the occurrence of the features in different spatial zones. Our classification results on the Caltech 101 dataset show significant improvements of up to 6% compared to previous improvements of the biologically inspired model of HMAX.