Segmentation of Color Textures Using K-Means Cluster Based Wavelet Image Fusion

Abstract:

Article Preview

This paper develops a new method for cluster segmentation of color texture image. At first, a color texture image has been decomposed three sub-bands that R (red), G (green), B (blue) channel. Second, each sub-bands channel has been decomposed by discrete wavelet transform (DWT) to the same resolution. Third, fusion operation is performed on the transformed images according to PCA- based (Principal Component Analysis) weighted average rule to choose the reconstructed coefficients. Then all the decomposed sub-bands images are reconstructed using inverse discrete wavelet transform (IDWT) that becoming a fusion image. Finally, k-means cluster segmentation algorithm has been applied for this fusion image, the segmentation results has been obtained. A number of experiments are performed to demonstrate the efficacy of the proposed method.

Info:

Periodical:

Edited by:

Qi Luo

Pages:

209-214

Citation:

Z. K. Huang et al., "Segmentation of Color Textures Using K-Means Cluster Based Wavelet Image Fusion", Applied Mechanics and Materials, Vols. 20-23, pp. 209-214, 2010

Online since:

January 2010

Export:

Price:

$38.00

[1] M. Tuceryan, A. K. Jain, Texture Analysis, Handbook of Pattern Recognition and Computer Vision (2nd ed. ), World Scientific Publishing Company, pp.207-248, (1998).

[2] Zhi-Kai Huang, Kwok-Wing Chau, Unsupervised Image Segmentation Using EM Algorithm by Histogram, Applied Mathematics and Computation,Vol. 205(2), pp.899-907, (2008).

DOI: https://doi.org/10.1016/j.amc.2008.05.130

[3] S. Mallat, A wavelet tour of signal processing, Academic Press Publishing Company, (1998).

[4] M. Turk and A. Pentland, Eigenfaces for recognition, J. Cogn Neurosci Vol. 3, 71-86. (1991).

[5] Jain A.K., Murty M.N., and Flynn P.J. Data clustering: A review. ACM Computing Surveys, 31(3): 264-323, September (1999).

DOI: https://doi.org/10.1145/331499.331504

[6] Shi J. and Malik J. Normalized cuts and image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(8): 888-905, (2000).

DOI: https://doi.org/10.1109/34.868688

[7] J. K. Aggarwal, Multisensor Fusion for Computer Vision. Springer Verlag, (1993).

[8] Zhi-Kai Huang, De-Hui Liu, Unsupervised Image Segmentation Using EM Algorithm by Histogram, Lecture Notes in Computer Science, Springer-Verlag, Vol. 4681, 1275-1282, (International Conference on Intelligent Computing, Qingdao, August 21-24, China) (2007).

DOI: https://doi.org/10.1007/978-3-540-74171-8_130

[9] http: /graphicssoft. about. com/od/photoshopdownloads/ig/Photoshop-Patterns/Wood-Pattern-Te xtures. htm.

[10] Wang, J.Z., Li, J., Gray, R.M. and Wiederhold, G, Unsupervised multiresolution segmentation for images with low depth of field, IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 1, pp.85-90, Jan. (2001).

DOI: https://doi.org/10.1109/34.899949

[11] J.G. Zhang, T. Tan, Brief Review of Invariant Texture Analysis Methods. Pattern Recognition, Vol. 35/3, pp.735-747, (2002).

DOI: https://doi.org/10.1016/s0031-3203(01)00074-7

[12] S. Arivazhagan, L. Ganesan, Texture segmentation using wavelet transform, Pattern Recognition Letters 24, pp.3197-3203, (2003).

DOI: https://doi.org/10.1016/j.patrec.2003.08.005

[13] D. A. Clausi, H. Deng, Design-based texture feature fusion using Gabor filters and co-occurrence probabilities, IEEE Transactions on Image Processing, Volume 14, Issue 7, pp.925-936, (2005).

DOI: https://doi.org/10.1109/tip.2005.849319

[14] Haindl, M. & Mikeš, S., Colour texture segmentation using modelling approach. Lecture Notes in Computer Science, (3687), pp.484-491. (2005).

DOI: https://doi.org/10.1007/11552499_54

[15] G. Piella. A general framework for multiresolution image fusion: from pixels to regions. Information Fusion, 4: pp.259-280, (2003).

DOI: https://doi.org/10.1016/s1566-2535(03)00046-0

Fetching data from Crossref.
This may take some time to load.