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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)
AbstractLocal 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.
KeywordBundled Features Image Representation Image Classification Codebook Feature Selection
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Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.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.
Recommended Citation
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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