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Image Data Classification using a Similarity Function based on Second Order Tensor  

Yoon, Dong-Woo (메디오피아테크 멀티미디어연구소)
Lee, Kwan-Yong (한국방송통신대학교 컴퓨터과학과)
Park, Hye-Young (경북대학교 전자전기컴퓨터학부)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.8, 2009 , pp. 664-672 More about this Journal
Abstract
Recently, studies on utilizing tensor expression on image data analysis and processing have been attracting much interest. The purpose of this study is to develop an efficient system for classifying image patterns by using second order tensor expression. To achieve the goal, we propose a data generation model expressed by class factors and environment factors with second order tensor representation. Based on the data generation model, we define a function for measuring similarities between two images. The similarity function is obtained by estimating the probability density of environment factors using a matrix normal distribution. Through computational experiments on a number of benchmark data sets, we confirm that we can make improvement in classification rates by using second order tensor, and that the proposed similarity function is more appropriate for image data compared to conventional similarity measures.
Keywords
Data generation model; Tensor analysis; Similarity function; Image data classification;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 K. Lee and H. Park, 'A New Similarity Measure Based on Intraclass Statistics for Biometric Systems,' ETRI Journal, 25(5), pp.401-406, 2003   DOI   ScienceOn
2 J. Dauxois, Y. Romain, and S. Viguier-Pla, 'Tensor Products and Statistics,' Linear Algebra Appl., pp.59-88, 1994   DOI   ScienceOn
3 J. Yang, D. Zhang, A. F. Frangi, and J. Yang, 'Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition,' IEEE Trans. on PAMI, 26(1), pp.131-137, 2004   DOI   ScienceOn
4 S. Yan, D. Xu, Q. Yang, L. Zhang, X. Tang and H. Zhang, 'Discriminant Analysis with Tensor Representation,' Proc. of CVPR2005, 1, pp.526-532, 2005
5 X. He, D. Cai, P. Niyogi, 'Tensor Subspace Analysis,' Advances in Neural Information Processing Systems, 2005
6 http://pics.psych.stir.ac.uk/
7 R. L. Gorsuch, Factor Analysis, Erlbaum, 1983
8 M, Alex. O. Vasilescu and D Terzopoulos, 'Multilinear Image Analysis for Facial Recognition,' Proc. of 16th International Conference on Pattern Recognition, 2, pp.511-514, 2002
9 M. A. O. Vasilescu and D Terzopoulos, 'Multilinear Subspace Analysis of Image Ensembles,' Proc. of CVPR2003, 2, pp.93-99, 2003
10 D. Cai, X. He, and J. Han, 'Subspace learning based on tensor analysis,' Tech. Rep. (UIUCDCSR- 2005-2572), Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Ill, USA, 2005
11 M. Li, B. Yuan, '2D-LDA: A Statistical Linear Discriminant Analysis for Image Matrix,' Pattern Recognition Letters, 26, pp.527-532, 2004   DOI
12 E. Alpaydin, Introduction to Machine Learning, MIT Press, 2004
13 S. H. Lee and S. Choi, 'Two-dimensional canonical correlation analysis,' IEEE Signal Processing Letters, 14(10), pp.735-738, 2007   DOI   ScienceOn
14 M. A. O. Vasilescu and D Terzopoulos, 'Multilinear Analysis of Image Ensembles : Tensor- Faces,' Proc. of ECCV2002, pp.447-460, 2002
15 M. Turk and A. P. Pentland, 'Face Recognition Using Eigenfaces,' Proc. of CVPR1991, pp.586-5131, 1991
16 K. Fukunaga, Introduction to Statistical Pattern Classification, Academic Press, San Diego, California, USA, 1990
17 조민국, 박혜영, '변형된 팩터 분석 모델을 이용한 생체데이타 분류 시스템', 정보과학회논문지 : 소프트웨어 및 응용, 34(7), pp.667-680, 2007   과학기술학회마을
18 T. Chen, Y. J Hsu, X. Liu, and W. Zhang, 'Principle Component Analysis and its Variants for Biometrics,' Image Processing 1, pp.61-64, 2002
19 http://www.itl.nist.gov/iad/humanid/feret/feret_master. html
20 http://cvc.yale.edu/projects/yalefaces/yalefaces.html