Image recommendation based on a novel biologically inspired hierarchical model

doi:10.1007/s11042-017-5514-z

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Image recommendation based on a novel biologically inspired hierarchical model
	Lu, Yan-Feng1 ; Qiao, Hong2,3 ; Li, Yi4 ; Jia, Li-Hao1
发表期刊	MULTIMEDIA TOOLS AND APPLICATIONS
	2018-02-01
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
DOI	10.1007/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	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/15332
专题	多模态人工智能系统全国重点实验室_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
第一作者单位	类脑智能研究中心
推荐引用方式 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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