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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/747105 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832551894581510144 |
|---|---|
| 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. |
| format | Article |
| id | doaj-art-a4a9f74893a34b1aa8be69c09087aac5 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| 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 |
| work_keys_str_mv | AT jingchen similaritymeasurelearninginclosedformsolutionforimageclassification AT yuanyantang similaritymeasurelearninginclosedformsolutionforimageclassification AT clphilipchen similaritymeasurelearninginclosedformsolutionforimageclassification AT binfang similaritymeasurelearninginclosedformsolutionforimageclassification AT zhaoweishang similaritymeasurelearninginclosedformsolutionforimageclassification AT yueweilin similaritymeasurelearninginclosedformsolutionforimageclassification |