Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment
This work addresses computational inefficiency in image recognition models for researchers and practitioners, but it is incremental as it builds on existing BIM frameworks.
The authors tackled the issue of random patch selection in Biologically Inspired Models (BIM) for image recognition, which causes heavy computational burden, by proposing a novel patch selection method using oriented Gaussian-Hermite moment (PSGHM) to enhance BIM into PBIM, resulting in significant performance improvements on datasets like CalTech05, TUD, and GRAZ01.
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.