Variance Based Component Analysis for Texture Segmentation
This paper presents a comparative analysis of a new
unsupervised PCA-based technique for steel plates texture segmentation
towards defect detection. The proposed scheme called Variance
Based Component Analysis or VBCA employs PCA for feature
extraction, applies a feature reduction algorithm based on variance of
eigenpictures and classifies the pixels as defective and normal. While
the classic PCA uses a clusterer like Kmeans for pixel clustering,
VBCA employs thresholding and some post processing operations to
label pixels as defective and normal. The experimental results show
that proposed algorithm called VBCA is 12.46% more accurate and
78.85% faster than the classic PCA.
[1] A. Monadjemi. Towards efficient texture classification and abnormality
detection. PhD Thesis, Department of Computer Science, University of
Bristol, October 2004.
[2] M. Turk, A. Pentland. Eigenfaces for Recognition. Journal of Cognitive
Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991.
[3] A. K. Jain, F. Farrokhnia. Unsupervised Texture Segmentation Using
Gabor Filters. Journal of Pattern Recognition, Vol. 24, No. 12, pp. 1167-
1186, 1991.
[4] Y. Chen, R. Wang. Texture Segmentation Using Independent Component
Analysis of Gabor Features. The 18th International Conference on Pattern
Recognition (ICPR-06), pp. 147-150, 2006.
[5] J. Shlens. A Tutorial on Principal Component Analysis.
http://www.snl.salk.edu/~ shlens/, 2009.
[6] X. Xie. A Review of Recent Advances in Surface Defect Detection using
Texture analysis Techniques. Electronic Letters on Computer Vision and
Image Analysis, Vol. 7, No. 2, pp. 1-22, 2008.
[7] A. Eleyan, H. Demirel. PCA and LDA based Neural Networks for Human
Face Recognition. Face Recognition, Book edited by: Kresimir Delac and
Mislav Grgic, June 2007.
[1] A. Monadjemi. Towards efficient texture classification and abnormality
detection. PhD Thesis, Department of Computer Science, University of
Bristol, October 2004.
[2] M. Turk, A. Pentland. Eigenfaces for Recognition. Journal of Cognitive
Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991.
[3] A. K. Jain, F. Farrokhnia. Unsupervised Texture Segmentation Using
Gabor Filters. Journal of Pattern Recognition, Vol. 24, No. 12, pp. 1167-
1186, 1991.
[4] Y. Chen, R. Wang. Texture Segmentation Using Independent Component
Analysis of Gabor Features. The 18th International Conference on Pattern
Recognition (ICPR-06), pp. 147-150, 2006.
[5] J. Shlens. A Tutorial on Principal Component Analysis.
http://www.snl.salk.edu/~ shlens/, 2009.
[6] X. Xie. A Review of Recent Advances in Surface Defect Detection using
Texture analysis Techniques. Electronic Letters on Computer Vision and
Image Analysis, Vol. 7, No. 2, pp. 1-22, 2008.
[7] A. Eleyan, H. Demirel. PCA and LDA based Neural Networks for Human
Face Recognition. Face Recognition, Book edited by: Kresimir Delac and
Mislav Grgic, June 2007.
@article{"International Journal of Information, Control and Computer Sciences:63102", author = "Zeinab Ghasemi and S. Amirhassan Monadjemi and Abbas Vafaei", title = "Variance Based Component Analysis for Texture Segmentation", abstract = "This paper presents a comparative analysis of a new
unsupervised PCA-based technique for steel plates texture segmentation
towards defect detection. The proposed scheme called Variance
Based Component Analysis or VBCA employs PCA for feature
extraction, applies a feature reduction algorithm based on variance of
eigenpictures and classifies the pixels as defective and normal. While
the classic PCA uses a clusterer like Kmeans for pixel clustering,
VBCA employs thresholding and some post processing operations to
label pixels as defective and normal. The experimental results show
that proposed algorithm called VBCA is 12.46% more accurate and
78.85% faster than the classic PCA.", keywords = "Principal Component Analysis; Variance Based Component
Analysis; Defect Detection; Texture Segmentation.", volume = "6", number = "1", pages = "124-4", }