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Hierarchical kernel-based rotation and scale invariant similarity
Y.Y. Tang1,2; Tian Xia2; Yantao Wei3; Hong Li4; Luoqing Li5
Source PublicationPattern Recognition

Image similarity measure has been widely used in pattern recognition and computer vision. We usually face challenges in terms of rotation and scale changes. In order to overcome these problems, an effective similarity measure which is invariant to rotation and scale is proposed in this paper. Firstly, the proposed method applies the log-polar transform to eliminate the rotation and scale effect and produces a row and column translated log-polar image. Then the obtained log-polar image is passed to hierarchical kernels to eliminate the row and column translation effects. In this way, the output of the proposed method is invariant to rotation and scale. The theoretical analysis of invariance has also been given. In addition, an effective template sets construction method is presented to reduce computational complexity and to improve the accuracy of the proposed similarity measure. Through the experiments with several image data sets we demonstrate the advantages of the proposed approach: high classification accuracy and fast. 

KeywordHierarchical Kernels Image Similarity Measure Log-polar Transform Rotation And Scale Invariance
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000331669300011
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Cited Times [WOS]:13   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorYantao Wei
Affiliation1.College of Computer Science, Chongqing University, China
2.Faculty of Science and Technology, University of Macau, Macau, China
3.School of Educational Information Technology, Central China Normal University, Wuhan 430079, China
4.School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China
5.Faculty of Mathematics and Computer Science, Hubei University, Wuhan 430062, China
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Y.Y. Tang,Tian Xia,Yantao Wei,et al. Hierarchical kernel-based rotation and scale invariant similarity[J]. Pattern Recognition,2014,47(4):1674-1688.
APA Y.Y. Tang,Tian Xia,Yantao Wei,Hong Li,&Luoqing Li.(2014).Hierarchical kernel-based rotation and scale invariant similarity.Pattern Recognition,47(4),1674-1688.
MLA Y.Y. Tang,et al."Hierarchical kernel-based rotation and scale invariant similarity".Pattern Recognition 47.4(2014):1674-1688.
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