Bundled local features for image representation

doi:10.1109/TCSVT.2017.2694060

CASIA OpenIR > 类脑智能研究中心

	Bundled local features for image representation
	Zhang CJ(张淳杰); Sang JT(桑基韬); Zhu GB(朱桂波); Tian Q(田奇)
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2017
期号	0 页码:0
摘要	Local features have been widely used for image representation. Traditional methods often treat each local feature independently or simply model the correlations of local features with spatial partition. However, local features are correlated and should be jointly modeled. Besides, due to the variety of images, pre-defined partition rules will probably introduce noisy information. To solve these problems, in this paper, we propose a novel bundled local features method for efficient image representation and apply it for classification. Specially, we first extract local features and bundle them together with over-complete spatial shapes by viewing each local feature as the central point. Then, the most discriminatively bundling features are selected by reconstruction error minimization. The encoding parameters are then used for image representations in a matrix form. Finally, we train bi-linear classifiers with quadratic hinge loss to predict the classes of images. The proposed method can combine local features appropriately and efficiently for discriminative representations. Experimental results on three image datasets show the effectiveness of the proposed method compared with other local features combination strategies.
关键词	Bundled Features Image Representation Image Classification Codebook Feature Selection
DOI	10.1109/TCSVT.2017.2694060
引用统计	被引频次：20[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/173211/15315
专题	类脑智能研究中心
作者单位	1.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.School of Computer and Control Engineering, University of Chinese Academy of Sciences, 100049, Beijing, China 3.School of Computer and Information Technology, Beijing Jiaotong University 4.Department of Computer Sciences, University of Texas at San Antonio. TX, 78249, U.S.A.
推荐引用方式 GB/T 7714	Zhang CJ,Sang JT,Zhu GB,et al. Bundled local features for image representation[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2017(0):0.
APA	Zhang CJ,Sang JT,Zhu GB,&Tian Q.(2017).Bundled local features for image representation.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY(0),0.
MLA	Zhang CJ,et al."Bundled local features for image representation".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY .0(2017):0.

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