{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T02:09:19Z","timestamp":1648951759931},"reference-count":34,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:p> 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\u2013Hermite 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. <\/jats:p>","DOI":"10.1142\/s0219691319400071","type":"journal-article","created":{"date-parts":[[2018,5,22]],"date-time":"2018-05-22T09:00:51Z","timestamp":1526979651000},"page":"1940007","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment"],"prefix":"10.1142","volume":"17","author":[{"given":"Yanfeng","family":"Lu","sequence":"first","affiliation":[{"name":"Research Center for Brain-Inspired Intelligence Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihao","family":"Jia","sequence":"additional","affiliation":[{"name":"Research Center for Brain-Inspired Intelligence Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Qiao","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation Chinese Academy of Sciences, Beijing 100190, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nanchang University, Nanchang 330031, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongshuai","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology, Beijing 100083, P. R. 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