CASIA OpenIR  > 类脑智能研究中心
Contextual Exemplar Classifier-Based Image Representation for Classification
Zhang, Chunjie1,2,3; Huang, Qingming2,3,4; Tian, Qi5
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
2017-08-01
Volume27Issue:8Pages:1691-1699
SubtypeArticle
AbstractThe use of local features for image representation has become popular in recent years. Local features are often used in the bag-of-visual-words scheme. Although proven effective, this method still has two drawbacks. First, local regions from which local features are extracted are not discriminative enough for visual tasks. Hence, the combination of local features is necessary. Second, the semantic gap between visual features and human perception also hinders the performance. To address these two problems, in this paper, we propose a novel contextual exemplar classifier-based method for image representation and apply it for classification tasks. Each exemplar classifier is trained to separate one training image from the other images of different classes. We partition each image into a number of regions and use the responses of these exemplar classifiers as the image region's representation. The contextual relationship is then modeled using mixture Dirichlet distributions. A bilayer model is used to predict image classes with L-2 constraints. Experimental results on the Natural Scene, Caltech-101/256, Flower-17/102, and SUN-397 data sets show that the proposed method is able to outperform the state-of-the-art local feature-based methods for image classification.
KeywordComputer Vision Image Processing Pattern Classification
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TCSVT.2016.2527380
WOS KeywordLOCAL FEATURES ; ADAPTATION ; RETRIEVAL ; TEXTURE ; SCENE
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61303154 ; National Basic Research Program of China (973 Program)(2012CB316400 ; Open Project of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences ; 61332016) ; 2015CB351802)
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000407400300007
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15313
Collection类脑智能研究中心
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
5.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
Recommended Citation
GB/T 7714
Zhang, Chunjie,Huang, Qingming,Tian, Qi. Contextual Exemplar Classifier-Based Image Representation for Classification[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2017,27(8):1691-1699.
APA Zhang, Chunjie,Huang, Qingming,&Tian, Qi.(2017).Contextual Exemplar Classifier-Based Image Representation for Classification.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,27(8),1691-1699.
MLA Zhang, Chunjie,et al."Contextual Exemplar Classifier-Based Image Representation for Classification".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 27.8(2017):1691-1699.
Files in This Item: Download All
File Name/Size DocType Version Access License
07401002.pdf(1939KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang, Chunjie]'s Articles
[Huang, Qingming]'s Articles
[Tian, Qi]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang, Chunjie]'s Articles
[Huang, Qingming]'s Articles
[Tian, Qi]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang, Chunjie]'s Articles
[Huang, Qingming]'s Articles
[Tian, Qi]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 07401002.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.