A novel visual codebook model based on fuzzy geometry for large-scale image classification

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	A novel visual codebook model based on fuzzy geometry for large-scale image classification
	Li, Yanshan 1; Huang, Qinghua 2,3; Xie, Weixin 1; Li, Xuelong4
刊名	pattern recognition
	2015-10-01
卷号	48 期号:10 页码:3125-3134
关键词	Codebook Fuzzy geometry Fuzzy set theory Image classification
英文摘要	the codebook model has been developed as an effective means for image classification. however, the inherent operation of assigning visual words to image feature vectors in traditional codebook approaches causes serious ambiguities in image classification. in particular, the nearest word may not be the best fit to a feature, and multiple words may be equally appropriate for one specific feature. to resolve these ambiguities, we propose a novel visual codebook model based on the n-dimensional fuzzy geometry (n-d fg) theory, where all visual words and features are modeled as fuzzy points in the n-d fg space, and appropriate uncertainty is introduced to each fuzzy point to enhance the representation capacity. this n-d fg-codebook model not only inherits advantages from the fuzzy set theory, but also facilitates the analysis and determination of the relationship between visual words and features in geometric form. by explicitly taking into account the ambiguities, we propose a novel measure of similarity between the visual words and fuzzy features. following the proposed codebook model and the novel similarity measure, we develop two useful image classification algorithms by modifying popular image coding algorithms (i.e. spm and llc). finally, experimental results demonstrate that the classification accuracy of the proposed algorithms is dramatically improved for a standard large-scale image database. for example, with a codebook size of 256, the proposed algorithms achieve similar performance as traditional algorithms with a codebook size of 1024, indicating that the proposed algorithms reduce the computational cost by 75% while achieving almost identical classification accuracy to traditional algorithms. thus, the proposed algorithms represent a more efficient and appropriate scheme for big image data. (c) 2015 elsevier ltd. all rights reserved.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; engineering, electrical & electronic
研究领域[WOS]	computer science ; engineering
关键词[WOS]	sparse representation ; plane geometry ; recognition ; segmentation ; set
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000357246100015
公开日期	2015-08-18
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/25145]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Shenzhen Univ, ATR Natl Key Lab Def Technol, Shenzhen 518060, Peoples R China 2.S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Guangdong, Peoples R China 3.Natl Engn Res Ctr Tissue Restorat & Reconstruct, Guangzhou, Guangdong, Peoples R China 4.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Li, Yanshan,Huang, Qinghua,Xie, Weixin,et al. A novel visual codebook model based on fuzzy geometry for large-scale image classification[J]. pattern recognition,2015,48(10):3125-3134.
APA	Li, Yanshan,Huang, Qinghua,Xie, Weixin,&Li, Xuelong.(2015).A novel visual codebook model based on fuzzy geometry for large-scale image classification.pattern recognition,48(10),3125-3134.
MLA	Li, Yanshan,et al."A novel visual codebook model based on fuzzy geometry for large-scale image classification".pattern recognition 48.10(2015):3125-3134.