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Similarity Measure Learning in Closed-Form Solution for Image Classification
Jing Chen1,2; Yuan Yan Tang1,2; C. L. Philip Chen1; Bin Fang2; Zhaowei Shang2; Yuewei Lin3
2014-06-26
Source PublicationThe Scientific World journal
ISSN2356-6140
Volume2014
Other Abstract

Adopting a measure is essential in many multimedia applications. Recently, distance learning is becoming an active research problem. In fact, the distance is the natural measure for dissimilarity. Generally, a pairwise relationship between two objects in learning tasks includes two aspects: similarity and dissimilarity. The similarity measure provides different information for pairwise relationships. However, similarity learning has been paid less attention in learning problems. In this work, firstly, we propose a general framework for similarity measure learning (SML). Additionally, we define a generalized type of correlation as a similarity measure. By a set of parameters, generalized correlation provides flexibility for learning tasks. Based on this similarity measure, we present a specific algorithm under the SML framework, called correlation similarity measure learning (CSML), to learn a parameterized similarity measure over input space. A nonlinear extension version of CSML, kernel CSML, is also proposed. Particularly, we give a closed-form solution avoiding iterative search for a local optimal solution in the high-dimensional space as the previous work did. Finally, classification experiments have been performed on face databases and a handwritten digits database to demonstrate the efficiency and reliability of CSML and KCSML. 

DOIhttp://dx.doi.org/10.1155/2014/747105
URLView the original
Indexed BySCI
Language英语
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000343492200001
PublisherHINDAWI PUBLISHING CORPORATION, 410 PARK AVENUE, 15TH FLOOR, #287 PMB, NEW YORK, NY 10022 USA
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被引频次[WOS]:1   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorJing Chen
Affiliation1.Faculty of Science and Technology, University of Macau, Taipa 999078, Macau
2.Chongqing University, Chongqing 400030, China
3.University of South Carolina, Columbia, SC 29208, USA
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
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GB/T 7714
Jing Chen,Yuan Yan Tang,C. L. Philip Chen,et al. Similarity Measure Learning in Closed-Form Solution for Image Classification[J]. The Scientific World journal,2014,2014.
APA Jing Chen,Yuan Yan Tang,C. L. Philip Chen,Bin Fang,Zhaowei Shang,&Yuewei Lin.(2014).Similarity Measure Learning in Closed-Form Solution for Image Classification.The Scientific World journal,2014.
MLA Jing Chen,et al."Similarity Measure Learning in Closed-Form Solution for Image Classification".The Scientific World journal 2014(2014).
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