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High-Order Distance-Based Multiview Stochastic Learning in Image Classification
Jun Yu1; Yong Rui4; Yuan Yan Tang2; Dacheng Tao3
2014-12
Source PublicationIEEE Transactions on Cybernetics
ISSN2168-2267
Volume44Issue:12Pages:2431 - 2442
Abstract

How do we find all images in a larger set of images which have a specific content? Or estimate the position of a specific object relative to the camera? Image classification methods, like support vector machine (supervised) and transductive support vector machine (semi-supervised), are invaluable tools for the applications of content-based image retrieval, pose estimation, and optical character recognition. However, these methods only can handle the images represented by single feature. In many cases, different features (or multiview data) can be obtained, and how to efficiently utilize them is a challenge. It is inappropriate for the traditionally concatenating schema to link features of different views into a long vector. The reason is each view has its specific statistical property and physical interpretation. In this paper, we propose a high-order distance-based multiview stochastic learning (HD-MSL) method for image classification. HD-MSL effectively combines varied features into a unified representation and integrates the labeling information based on a probabilistic framework. In comparison with the existing strategies, our approach adopts the high-order distance obtained from the hypergraph to replace pairwise distance in estimating the probability matrix of data distribution. In addition, the proposed approach can automatically learn a combination coefficient for each view, which plays an important role in utilizing the complementary information of multiview data. An alternative optimization is designed to solve the objective functions of HD-MSL and obtain different views on coefficients and classification scores simultaneously. Experiments on two real world datasets demonstrate the effectiveness of HD-MSL in image classification. 

KeywordHypergraph Image Classification Multiview Stochastic Probability Matrix
DOIhttps://doi.org/10.1109/TCYB.2014.2307862
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000345629000016
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
The Source to ArticleScopus
Fulltext Access
Citation statistics
Cited Times [WOS]:211   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorJun Yu; Yong Rui; Yuan Yan Tang; Dacheng Tao
Affiliation1.School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
2.Department of Computer and Information Science, University of Macau, Macau
3.Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
4.Microsoft Research Asia, Beijing 100080, China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jun Yu,Yong Rui,Yuan Yan Tang,et al. High-Order Distance-Based Multiview Stochastic Learning in Image Classification[J]. IEEE Transactions on Cybernetics,2014,44(12):2431 - 2442.
APA Jun Yu,Yong Rui,Yuan Yan Tang,&Dacheng Tao.(2014).High-Order Distance-Based Multiview Stochastic Learning in Image Classification.IEEE Transactions on Cybernetics,44(12),2431 - 2442.
MLA Jun Yu,et al."High-Order Distance-Based Multiview Stochastic Learning in Image Classification".IEEE Transactions on Cybernetics 44.12(2014):2431 - 2442.
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