Similarity Measure Learning in Closed-Form Solution for Image Classification

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: similari...

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Main Authors: Jing Chen, Yuan Yan Tang, C. L. Philip Chen, Bin Fang, Zhaowei Shang, Yuewei Lin
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2014/747105
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author Jing Chen
Yuan Yan Tang
C. L. Philip Chen
Bin Fang
Zhaowei Shang
Yuewei Lin
author_facet Jing Chen
Yuan Yan Tang
C. L. Philip Chen
Bin Fang
Zhaowei Shang
Yuewei Lin
author_sort Jing Chen
collection DOAJ
description 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.
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institution Kabale University
issn 2356-6140
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language English
publishDate 2014-01-01
publisher Wiley
record_format Article
series The Scientific World Journal
spelling doaj-art-a4a9f74893a34b1aa8be69c09087aac52025-02-03T06:00:08ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/747105747105Similarity Measure Learning in Closed-Form Solution for Image ClassificationJing Chen0Yuan Yan Tang1C. L. Philip Chen2Bin Fang3Zhaowei Shang4Yuewei Lin5Faculty of Science and Technology, University of Macau, Taipa 999078, MacauFaculty of Science and Technology, University of Macau, Taipa 999078, MacauFaculty of Science and Technology, University of Macau, Taipa 999078, MacauChongqing University, Chongqing 400030, ChinaChongqing University, Chongqing 400030, ChinaUniversity of South Carolina, Columbia, SC 29208, USAAdopting 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.http://dx.doi.org/10.1155/2014/747105
spellingShingle Jing Chen
Yuan Yan Tang
C. L. Philip Chen
Bin Fang
Zhaowei Shang
Yuewei Lin
Similarity Measure Learning in Closed-Form Solution for Image Classification
The Scientific World Journal
title Similarity Measure Learning in Closed-Form Solution for Image Classification
title_full Similarity Measure Learning in Closed-Form Solution for Image Classification
title_fullStr Similarity Measure Learning in Closed-Form Solution for Image Classification
title_full_unstemmed Similarity Measure Learning in Closed-Form Solution for Image Classification
title_short Similarity Measure Learning in Closed-Form Solution for Image Classification
title_sort similarity measure learning in closed form solution for image classification
url http://dx.doi.org/10.1155/2014/747105
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AT yuanyantang similaritymeasurelearninginclosedformsolutionforimageclassification
AT clphilipchen similaritymeasurelearninginclosedformsolutionforimageclassification
AT binfang similaritymeasurelearninginclosedformsolutionforimageclassification
AT zhaoweishang similaritymeasurelearninginclosedformsolutionforimageclassification
AT yueweilin similaritymeasurelearninginclosedformsolutionforimageclassification