Image recommendation based on a novel biologically inspired hierarchical model
Lu, Yan-Feng1; Qiao, Hong2,3; Li, Yi4; Jia, Li-Hao1
2018-02-01
发表期刊MULTIMEDIA TOOLS AND APPLICATIONS
卷号77期号:4页码:4323-4337
文章类型Article
摘要Image 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.
关键词Image Recommendation Classification Biologically Inspired Model Image Retrieval Feature Representation
WOS标题词Science & Technology ; Technology
DOI10.1007/s11042-017-5514-z
关键词[WOS]ORTHOGONAL MATCHING PURSUIT ; OBJECT RECOGNITION ; RECEPTIVE FIELDS ; RETRIEVAL ; FEATURES ; CORTEX ; SCENE
收录类别SCI
语种英语
项目资助者National 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研究方向Computer Science ; Engineering
WOS类目Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000425296500016
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15332
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
作者单位1.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
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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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