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Biologically inspired model (BIM) for image recognition is a robustcomputational architecture, which has attracted widespread attention. BIM canbe described as a four-layer structure based on the mechanisms of the visualcortex. Although the performance of BIM for image recognition is robust, ittakes the randomly selected ways for the patch selection, which is sightless,and results in heavy computing burden. To address this issue, we propose anovel patch selection method with oriented Gaussian-Hermite moment (PSGHM), andwe enhanced the BIM based on the proposed PSGHM, named as PBIM. In contrast tothe conventional BIM which adopts the random method to select patches withinthe feature representation layers processed by multi-scale Gabor filter banks,the proposed PBIM takes the PSGHM way to extract a small number ofrepresentation features while offering promising distinctiveness. To show theeffectiveness of the proposed PBIM, experimental studies on objectcategorization are conducted on the CalTech05, TU Darmstadt (TUD), and GRAZ01databases. Experimental results demonstrate that the performance of PBIM is asignificant improvement on that of the conventional BIM.

Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment