Image classification by search with explicitly and implicitly semantic representations
Zhang, Chunjie1,2; Zhu, Guibo3; Huang, Qingming1,2,4; Tian, Qi5
2017-01-10
发表期刊INFORMATION SCIENCES
卷号376期号:0页码:125-135
文章类型Article
摘要Image classification refers to the task of automatically classifying the categories of images based on the contents. This task is typically solved using visual features with the histogram based classification scheme. Although effective, this strategy has two drawbacks. On one hand, histogram based representation often disregards the object layout which is very important for classification. On the other hand, visual features are unable to fully separate different images due to the semantic gap. To solve these two problems, in this paper, we propose a novel image classification method by explicitly and implicitly representing the images with searching strategy. First, to make use of object layouts, we randomly select a number of regions and then use these regions for image representations. Second, we generate the explicitly semantic representations using a number of pre-learned semantic models. Third, we measure the visual similarities with the Internet images and use the text information for implicitly semantic representations. Since Internet images are contaminated with noise, the resulting representations only implicitly reflect the contents of images. Finally, both the explicitly and implicitly semantic representations are jointly modeled for image classifications by training bi-linear classifiers. We evaluate the effectiveness of the proposed image classification by search with explicitly and implicitly semantic representations method (EISR) on the Scene-15 dataset, the MIT-Indoor dataset, the UIUC-Sports dataset and the PASCAL VOC 2007 dataset. The experimental results prove the usefulness of the proposed method. (C) 2016 Elsevier Inc. All rights reserved.
关键词Explicit Representation Implicit Representation Semantic Modeling Image Classification
WOS标题词Science & Technology ; Technology
DOI10.1016/j.ins.2016.10.019
关键词[WOS]SCENE CLASSIFICATION ; LOCAL FEATURES ; LOW-RANK ; RECOGNITION ; CATEGORIZATION ; ANNOTATION ; SPACE
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61303154)
WOS研究方向Computer Science
WOS类目Computer Science, Information Systems
WOS记录号WOS:000388545100009
引用统计
被引频次:6[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/173211/13351
专题模式识别国家重点实验室_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.Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100864, Peoples R China
3.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intell Info Proc, Beijing 100190, Peoples R China
5.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
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Zhang, Chunjie,Zhu, Guibo,Huang, Qingming,et al. Image classification by search with explicitly and implicitly semantic representations[J]. INFORMATION SCIENCES,2017,376(0):125-135.
APA Zhang, Chunjie,Zhu, Guibo,Huang, Qingming,&Tian, Qi.(2017).Image classification by search with explicitly and implicitly semantic representations.INFORMATION SCIENCES,376(0),125-135.
MLA Zhang, Chunjie,et al."Image classification by search with explicitly and implicitly semantic representations".INFORMATION SCIENCES 376.0(2017):125-135.
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