From image vector to matrix: A straightforward image projection technique – IMPCA vs. PCA. (English) Zbl 1006.68865
Summary: The conventional principal component analysis (PCA) and Fisher linear discriminant analysis (FLD) are both based on vectors. Rather, in this paper, a novel PCA technique directly based on original image matrices is developed for image feature extraction. Experimental results on ORL face database show that the proposed IMPCA are more powerful and efficient than conventional PCA and FLD.
Keywords:
image principal component analysis (IMPCA); principal component analysis (PCA); linear discriminant analysis (FLD); image feature extractionSoftware:
ORL faceReferences:
| [1] | M. Turk, A. Pentland, Face recognition using Eigenfaces, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-591.; M. Turk, A. Pentland, Face recognition using Eigenfaces, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-591. |
| [2] | Belhumeur, P. N.; Hespanha, J. P.; Kriengman, D. J., Eigenfaces vs. Fisherfacesrecognition using class specific linear projection, IEEE Trans. Pattern Anal. Mach. Intell., 19, 7, 711-720 (1997) |
| [3] | Liu, K.; Cheng, Y.-Q.; Yang, J.-Y., Algebraic feature extraction for image recognition based on an optimal discriminant criterion, Pattern Recognition, 26, 6, 903-911 (1993) |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
