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

TL;DR

This paper introduces PBIM, an enhanced biologically inspired image recognition model that uses a novel patch selection method with oriented Gaussian-Hermite moments to improve performance and reduce computational load.

Contribution

The paper proposes a new patch selection technique using oriented Gaussian-Hermite moments to enhance BIM's efficiency and accuracy in image recognition tasks.

Findings

PBIM outperforms conventional BIM on multiple datasets.

The proposed method reduces computational burden.

Experimental results show significant accuracy improvements.

Abstract

Biologically inspired model (BIM) for image recognition is a robust computational architecture, which has attracted widespread attention. BIM can be described as a four-layer structure based on the mechanisms of the visual cortex. Although the performance of BIM for image recognition is robust, it takes the randomly selected ways for the patch selection, which is sightless, and results in heavy computing burden. To address this issue, we propose a novel patch selection method with oriented Gaussian-Hermite moment (PSGHM), and we enhanced the BIM based on the proposed PSGHM, named as PBIM. In contrast to the conventional BIM which adopts the random method to select patches within the feature representation layers processed by multi-scale Gabor filter banks, the proposed PBIM takes the PSGHM way to extract a small number of representation features while offering promising distinctiveness. To show the effectiveness of the proposed PBIM, experimental studies on object categorization are conducted on the CalTech05, TU Darmstadt (TUD), and GRAZ01 databases. Experimental results demonstrate that the performance of PBIM is a significant improvement on that of the conventional BIM.