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Structured Weak Semantic Space Construction For Visual Categorization
Zhang CJ(张淳杰); Cheng J(程健); Tian Q(田奇)
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
2017
Issue0Pages:0
AbstractVisual features have been widely used for image representation and categorization. However, visual features are often inconsistent with human perception. Besides, constructing explicit semantic space is still an open problem. To alleviate these two problems, in this paper, we propose to construct structured weak semantic space for image representation. Exemplar classifier is first trained to separate each training image from other images for weak semantic space construction. However, each exemplar classifier separates one training image from other images, it only has limited semantic separability. Besides, the outputs of exemplar classifiers are inconsistent with each other. We jointly construct the weak semantic space using structured constraint. This is achieved by imposing low-rank constraint on the outputs of exemplar classifiers with sparsity constraint. An alternative optimization procedure is used to learn the exemplar classifiers. Since the proposed method does not dependent on the initial image representation strategy, We can make use of various visual features for efficient exemplar classifier training (e.g. fisher vector based methods and convolutional neural networks based methods). We apply the proposed structured weak semantic space based image representation method for categorization. The experimental results on several public image datasets prove the effectiveness of the proposed method.
KeywordWeak Semantic Space Structure Learning Exemplar Classifier Training Visual Categorization Image Classification
DOI10.1109/TNNLS. 2017.2728060
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15317
Collection类脑智能研究中心
Affiliation1.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
3.Department of Computer Sciences, University of Texas at San Antonio. TX, 78249-1604, U.S.A.
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GB/T 7714
Zhang CJ,Cheng J,Tian Q. Structured Weak Semantic Space Construction For Visual Categorization[J]. IEEE Transactions on Neural Networks and Learning Systems,2017(0):0.
APA Zhang CJ,Cheng J,&Tian Q.(2017).Structured Weak Semantic Space Construction For Visual Categorization.IEEE Transactions on Neural Networks and Learning Systems(0),0.
MLA Zhang CJ,et al."Structured Weak Semantic Space Construction For Visual Categorization".IEEE Transactions on Neural Networks and Learning Systems .0(2017):0.
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