Image classification based on convolutional neural networks with cross-level strategy

doi:10.1007/s11042-016-3540-x

CORC > 合肥物质科学研究院 > 中国科学院合肥物质科学研究院 > 中科院合肥智能机械研究所

	Image classification based on convolutional neural networks with cross-level strategy
	Liu, Yu 1; Yin, Baocai 1; Yu, Jun 1; Wang, Zengfu 1,2
刊名	MULTIMEDIA TOOLS AND APPLICATIONS
	2017-04-01
卷号	76 期号:8 页码:11065-11079
关键词	Convolutional Neural Networks (Cnns) Image Classification Network Architecture Feature Representation Deep Learning
DOI	10.1007/s11042-016-3540-x
文献子类	Article
英文摘要	In the past few years, convolutional neural networks (CNNs) have exhibited great potential in the field of image classification. In this paper, we present a novel strategy named cross-level to improve the existing networks' architecture in which different levels of feature representation in a network are merely connected in series. The basic idea of cross-level is to establish a convolutional layer between two nonadjacent levels, aiming to extract more sufficient features with multiple scales at each feature representation level. The proposed cross-level strategy can be naturally integrated into an existing network without any change on its original architecture, which makes it very practical and convenient. Four popular convolutional networks for image classification are employed to illustrate its implementation in detail. Experimental results on the dataset adopted by the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) verify the effectiveness of the cross-level strategy on image classification. Furthermore, a new convolutional network with cross-level architecture is presented to demonstrate the potential of the proposed strategy in future network design.
WOS关键词	REPRESENTATION ; RECOGNITION ; FEATURES ; SCALE
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000400570400043
资助机构	National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; 61303150) ; 61303150) ; 61303150) ; 61303150) ; 61303150) ; 61303150) ; 61303150) ; 61303150) ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Natural Science Foundation of China(61472393 ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; National Science and Technology Major Project of the Ministry of Science and Technology of China(2012GB102007) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; Anhui Province Initiative Funds on Intelligent Speech Technology and Industrialization(13Z02008) ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; IFLYTEK CO.,LTD. ; 61303150) ; 61303150) ; 61303150) ; 61303150) ; 61303150) ; 61303150) ; 61303150) ; 61303150)
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/33471]
专题	合肥物质科学研究院_中科院合肥智能机械研究所
作者单位	1.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yu,Yin, Baocai,Yu, Jun,et al. Image classification based on convolutional neural networks with cross-level strategy[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2017,76(8):11065-11079.
APA	Liu, Yu,Yin, Baocai,Yu, Jun,&Wang, Zengfu.(2017).Image classification based on convolutional neural networks with cross-level strategy.MULTIMEDIA TOOLS AND APPLICATIONS,76(8),11065-11079.
MLA	Liu, Yu,et al."Image classification based on convolutional neural networks with cross-level strategy".MULTIMEDIA TOOLS AND APPLICATIONS 76.8(2017):11065-11079.