CASIA OpenIR
Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment
Lu, Yanfeng1; Jia, Lihao1; Qiao, Hong2; Li, Yi3; Qi, Zongshuai4
Source PublicationINTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING
ISSN0219-6913
2019-03-01
Volume17Issue:2Pages:16
Corresponding AuthorLu, Yanfeng(yanfeng.lv@ia.ac.cn)
AbstractBiologically 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.
KeywordImage recognition classification BIM oriented Gaussian-Hermite moment Gabor features patch selection
DOI10.1142/S0219691319400071
WOS KeywordOBJECT RECOGNITION ; FACE RECOGNITION ; APPEARANCE ; FEATURES
Indexed BySCI
Language英语
Funding ProjectNational Science Foundation of China[61603389] ; National Natural Science Foundation of China[61502494] ; National Natural Science Foundation of China[61210009] ; Strategic Priority Research Program of the CAS[XDB02080003] ; Development of Science and Technology of Guangdong Province Special Fund Project[2016B090910001]
Funding OrganizationNational Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the CAS ; Development of Science and Technology of Guangdong Province Special Fund Project
WOS Research AreaComputer Science ; Mathematics
WOS SubjectComputer Science, Software Engineering ; Mathematics, Interdisciplinary Applications
WOS IDWOS:000462661200008
PublisherWORLD SCIENTIFIC PUBL CO PTE LTD
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23487
Collection中国科学院自动化研究所
Corresponding AuthorLu, Yanfeng
Affiliation1.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
3.Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China
4.Univ Sci & Technol, Sch Comp & Commun Engn, Beijing 100083, Peoples R China
Recommended Citation
GB/T 7714
Lu, Yanfeng,Jia, Lihao,Qiao, Hong,et al. Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment[J]. INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING,2019,17(2):16.
APA Lu, Yanfeng,Jia, Lihao,Qiao, Hong,Li, Yi,&Qi, Zongshuai.(2019).Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment.INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING,17(2),16.
MLA Lu, Yanfeng,et al."Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment".INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 17.2(2019):16.
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