Undoing the codebook bias by linear transformation with sparsity and F-norm constraints for image classification

	Undoing the codebook bias by linear transformation with sparsity and F-norm constraints for image classification
	Zhang, Chunjie1 ; Liang, Chao 2; Pang, Junbiao 3; Zhang, Yifan4 ; Liu, Jing4 ; Qin, Lei 5; Huang, Qingming 1,5
发表期刊	PATTERN RECOGNITION LETTERS
	2014-08-01
期号	45 页码:197-204
文章类型	Article
摘要	The bag of visual words model (BoW) and its variants have demonstrated their effectiveness for visual applications. The BoW model first extracts local features and generates the corresponding codebook where the elements of a codebook are viewed as visual words. However, the codebook is dataset dependent and has to be generated for each image dataset. Besides, when we only have a limited number of training images, the codebook generated correspondingly may not be able to encode images well. This requires a lot of computational time and weakens the generalization power of the BoW model. To solve these problems, in this paper, we propose to undo the dataset bias by linear codebook transformation in an unsupervised manner. To represent each point in the local feature space, we need a number of linearly independent basis vectors. We view the codebook as a linear transformation of these basis vectors. In this way, we can transform the pre-learned codebooks for a new dataset using the pseudo-inverse of the transformation matrix. However, this is an under-determined problem which may lead to many solutions. Besides, not all of the visual words are equally important for the new dataset. It would be more effective if we can make some selection and choose the discriminative visual words for transformation. Specifically, the sparsity constraints and the F-norm of the transformation matrix are used in this paper. We propose an alternative optimization algorithm to jointly search for the optimal linear transformation matrixes and the encoding parameters. The proposed method needs no labeled images from either the source dataset or the target dataset. Image classification experimental results on several image datasets show the effectiveness of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.
关键词	Codebook Bias Linear Transformation Sparsity Alternative Optimization
WOS标题词	Science & Technology ; Technology
关键词[WOS]	OBJECT RECOGNITION ; REPRESENTATION ; CATEGORIZATION ; TEXTURE ; MODEL
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000337219200026
引用统计	被引频次：1[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/173211/3373
专题	紫东太初大模型研究中心_图像与视频分析
作者单位	1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 2.Wuhan Univ, Sch Comp, Natl Engn Res Ctr Multimedia Software, Wuhan 430072, Peoples R China 3.Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intell Info Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Chunjie,Liang, Chao,Pang, Junbiao,et al. Undoing the codebook bias by linear transformation with sparsity and F-norm constraints for image classification[J]. PATTERN RECOGNITION LETTERS,2014(45):197-204.
APA	Zhang, Chunjie.,Liang, Chao.,Pang, Junbiao.,Zhang, Yifan.,Liu, Jing.,...&Huang, Qingming.(2014).Undoing the codebook bias by linear transformation with sparsity and F-norm constraints for image classification.PATTERN RECOGNITION LETTERS(45),197-204.
MLA	Zhang, Chunjie,et al."Undoing the codebook bias by linear transformation with sparsity and F-norm constraints for image classification".PATTERN RECOGNITION LETTERS .45(2014):197-204.

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