Simple yet effective color principal and discriminant feature extraction for representing and recognizing color images

Neurocomputing

Volumn 149, Issue PB, 2015, Pages 1058-1073

  Zhang, Zhao ^a   Zhao, Mingbo ^b   Li, Bing ^b   Tang, Peng ^b   Li, Fan Zhang ^a  

^a SOOCHOW UNIVERSITY   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Color image analysis; Color image feature extraction; Dimensionality reduction; Discriminant analysis; Eigen decomposition; Principal component analysis

Indexed keywords

COLOR; COLOR IMAGE PROCESSING; DISCRIMINANT ANALYSIS; EXTRACTION; FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE RETRIEVAL; MATRIX ALGEBRA;

COLOR IMAGE ANALYSIS; COLOR IMAGES; DIMENSIONALITY REDUCTION; DISCRIMINANT FEATURE EXTRACTION; EFFECTIVENESS AND EFFICIENCIES; EIGEN DECOMPOSITION; LINEAR DISCRIMINANT ANALYSIS; ORTHOGONAL PROJECTION MATRICES;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COLOR; COLOR IMAGE FEATURE EXTRACTION ALGORITHM; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; IMAGE RETRIEVAL; LINEAR DISCRIMINANT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS;

EID: 85027957084     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.07.028     Document Type: Article

References (44)

1. Martinez A.M., Kak A.C. PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 2001, 23(2):228-233.
2. Mardia K., Kent J., Bibby J. Multivariate Analysis 1980, Academic Press, London.
3. Yang J., Zhang D., Frangi A.F., Yang J.Y. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26(4):131-137.
4. H. Kong, E.K. Teoh, J.G. Wang, R. Venkateswarlu, Two dimensional fisher discriminant analysis: forget about small sample size problem, in: Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2005, pp.761-764.
5. J.P. Ye, Least squares linear discriminant analysis, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 1087-1093.
6. Liu C.J., Yang J. ICA color space for pattern recognition. IEEE Trans. Neural Netw. 2009, 20(2):248-257.
7. Chen L., Man H., Nefian A.V. Face recognition based multi-class mapping of Fisher scores. Pattern Recogn. 2005, 38:799-811.
8. Silvén O., Niskanen M., Kauppinen H. Wood inspection with non-supervised clustering,. Mach. Vis. Appl. 2003, 13:275-285.
9. Lee E.S., Kang M.G. Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration,. IEEE Trans. Image Process. 2003, 12(7):826-837.
10. M. Thomas, C. Kambhamettu, S. Kumar, Face recognition using a color subspace LDA approach, in: Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence, 2008, pp. 231-235.
11. Chen S.C., Zhu Y.L., Zhang D.Q., Yang J. Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recogn. Lett. 2005, 26(8):1157-1167.
12. Li X., Lin S., Yan S., Xu D. Discriminant locally linear embedding with high order tensor data. IEEE Trans. Syst. Man Cybernet. Part B 2008, 38(2):342-352.
13. Zhang Z., Chow T.W.S. Maximum margin multisurface support tensor machines with application to image classification and segmentation. Expert Syst. Appl. 2012, 39(1):850-861.
14. He X., Yan S., Hu Y., Niyogi P., Zhang H. Face recognition using Laplacianfaces. IEEE Trans. Patten Anal. Mach. Intell. 2005, 27(3):328-340.
15. M. Thomas, C. Kambhamettu, S. Kumar, Face recognition using a color PCA framework, in: Proceedings of the 6th International Conference on Computer Vision Systems, 2008, pp. 373-382.
16. Hamsici O.C., Martinez A.M. Bayes optimality in linear discriminant analysis,. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30(4):647-657.
17. Lathauwer L., Moor B., Vandewalle J. A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 2000, 21(4):1253-1278.
18. Cheng H.D., Jiang X.H., Sun A., Wang J.L. Color image segmentation: advances and prospects. Pattern Recogn. 2001, 34(12):2259-2281.
19. Z. Zhang, M.B. Zhao, B. Li, P. Tang, ColorPCA: color principal feature extraction technique for color image reconstruction and recognition, in: Proceedings of the IEEE International Joint Conference on Neural Networks, Dallas, Texas, 2013.
20. Wang S.J., Yang J., Zhang N., Zhou C.G. Tensor discriminant color space for face recognition. IEEE Trans. Image Process. 2011, 20(9):2490-2501.
21. H. Yu, M. Bennamoun, 1D-PCA, 2D-PCA to nD-PCA, in: Proceedings of the International Conference of Pattern Recognition, 4, 2006, pp. 181-184.
22. Ye J.P., Li Q. A two-stage linear discriminant analysis via QR decomposition,. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27(6):929-941.
23. Xu D., Yan S., Lin S., Huang T.S., Chang S.F. Enhancing bilinear subspace learning by element rearrangement. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 32(10):1913-1920.
24. Liu C. Learning the uncorrelated, independent, and discriminating color spaces for face recognition,. IEEE Trans. Inf. Forensics Secur. 2008, 3(2):213-222.
25. Torres L., Reutter J.Y., Lorente L. The importance of the color information in face recognition,. Proc. Int. Conf. Image Process 1999, 3:627-631.
26. Yu S.Q., Tan T.N., Huang K.Q., Jia K., Wu X.Y. A study on gait-based gender classification. IEEE Trans. on image Processing 2009, 18(8):1905-1910.
27. Malagon-Borja L., Fuentes O. Object detection using image reconstruction with PCA,. Image Vis. Comput. 2009, 27:2-9.
28. Liu C.J. Effective use of color information for large scale face verification,. Neurocomputing 2013, 101:43-51.
29. Zhang D.Q., Zhou Z.H. (2D)2PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition,. Neurocomputing 2005, 69:224-231.
30. Zhang Z., Chow T.W.S. Tensor locally linear discriminative analysis. IEEE Signal Process. Lett. 2011, 18(11):843-846.
31. Tucker R.L. Some mathematical notes on the three-mode factor analysis,. Psychometrika 1966, 31:279-311.
32. Li M., Yuan B.Z. 2D-LDA: a statistical linear discriminant analysis for image matrix,. Pattern Recogn. Lett. 2005, 26(5):527-532.
33. Zhao C.R., Miao D.Q., Lai Z.H., Gao C., Liu C.C., Yang J.Y. Two-dimensional color uncorrelated discriminant analysis for face recognitionm. Neurocomputing 2013, 113:251-261.
34. Jing X.Y., Li S.L., Lan C.L., Zhang D., Yang J.Y., Liu Q. Color image canonical correlation analysis for face feature extraction and recognition. Signal Process. 2011, 91(8):2132-2140.
35. Liu Z.M., Yang J., Liu C.J. Extracting multiple features in the CID color space for face recognition. IEEE Trans. Image Process. 2010, 19(9):2502-2509.
36. Belkin M., Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation,. Neural Comput. 2003, 15(6):1373-1396.
37. Zhang Z., Chow T.W.S. Robust linearly optimized discriminant analysis. Neurocomputing 2012, 79:140-157.
38. Jin Z., Yang J.Y., Hu Z.S., Lou Z. Face recognition based on uncorrelated discriminant transformation. Pattern Recogn. 2001, 34(7):1405-1416.
39. V. Vonikakis, D. Chrysostomou, R. Kouskouridas, A. Gasteratos, Improving the robustness in feature detection by local contrast enhancement, in: Proceedings of the IEEE International Conference on Imaging Systems and Techniques, Manchester, United Kingdom, 2012, pp. 158-163.
40. Yang J., Liu C.J. Color image discriminant models and algorithms for face recognition. IEEE Trans. Neural Netw. 2008, 19(12):2088-2098.
41. Roweis S., Saul L. Nonlinear dimensionality reduction by locally linear embedding,. Science 2000, 290(5500):2323-2326.
42. Yang Y., Nie.F.P., Xu D., Luo J., Zhuang Y.T., Pan Y. A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 34(4):723-742.
43. Jia Y.Q., Nie F.P., Zhang C.S. Trace ratio problem revisited. IEEE Trans. Neural Netw. 2009, 20(4):729-735.
44. H. Wang, S. Yan, D. Xu, X. Tang, T. Huang, Trace ratio vs. ratio trace for dimensionality reduction, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, pp. 1-8, 2007.