Image recommendation based on a novel biologically inspired hierarchical model
Lu, Yan-Feng1; Qiao, Hong2,3; Li, Yi4; Jia, Li-Hao1
Source PublicationMULTIMEDIA TOOLS AND APPLICATIONS
2018-02-01
Volume77Issue:4Pages:4323-4337
SubtypeArticle
AbstractImage recommendation has become an increasingly relevant problem recently, since strong demand to quickly find interested images from vast amounts of image library. We describe a biologically inspired hierarchical model for image recommendation. The biologically inspired model (BIM) for invariant feature representation has attracted widespread attention, which approximately follows the organization of cortex visuel. BIM is a computation architecture with four layers. With the image data size increases, the four-layer framework is prone to be overfitting, which limits its application. To address this issue, we propose a biologically inspired hierarchical model (BIHM) for feature representation, which adds two more discriminative layers upon the conventional four-layer framework. In contrast to the conventional BIM that mimics the inferior temporal cortex, which corresponds to the low level feature, the proposed BIHM adds two more layers upon the conventional framework to simulate inferotemporal cortex, exploring higher level feature invariance and selectivity. Furthermore, we firstly utilize the BIHM in the image recommendation. To demonstrate the effectiveness of proposed model, we use it to image classification and retrieval tasks and perform experiments on CalTech5, Imagenet and CalTech256 datasets. The experiment results show that BIHM exhibits better performance than the conventional model in the tasks and is very comparable to existing architectures.
KeywordImage Recommendation Classification Biologically Inspired Model Image Retrieval Feature Representation
WOS HeadingsScience & Technology ; Technology
DOI10.1007/s11042-017-5514-z
WOS KeywordORTHOGONAL MATCHING PURSUIT ; OBJECT RECOGNITION ; RECEPTIVE FIELDS ; RETRIEVAL ; FEATURES ; CORTEX ; SCENE
Indexed BySCI
Language英语
Funding OrganizationNational Science Foundation of China(61603389) ; National Natural Science Foundation of China(61502494 ; Strategic Priority Research Program of the CAS(XDB02080003) ; Development of Science and Technology of Guangdong Province Special Fund Project(2016B090910001) ; 61210009)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000425296500016
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15332
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Affiliation1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai, Peoples R China
4.Nanchang Univ, Sch Informat Engn, Nanchang, Jiangxi, Peoples R China
Recommended Citation
GB/T 7714
Lu, Yan-Feng,Qiao, Hong,Li, Yi,et al. Image recommendation based on a novel biologically inspired hierarchical model[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2018,77(4):4323-4337.
APA Lu, Yan-Feng,Qiao, Hong,Li, Yi,&Jia, Li-Hao.(2018).Image recommendation based on a novel biologically inspired hierarchical model.MULTIMEDIA TOOLS AND APPLICATIONS,77(4),4323-4337.
MLA Lu, Yan-Feng,et al."Image recommendation based on a novel biologically inspired hierarchical model".MULTIMEDIA TOOLS AND APPLICATIONS 77.4(2018):4323-4337.
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