Extracting True Number of Clusters for Segmenting Image through Adaptive Finite Gaussian Mixture Model

M Masroor Ahmad, Sajid Naeem, Syed Muhammad Rehman Habib


Knowing exact number of clusters in a digital image significantly facilitates in precisely clustering an image. This paper proposes a new technique for extracting exact number of clusters from grey scale images. It analyzes the contents of the input image and adaptively reserves one distinct cluster for one distinct grey value. The total count of the grey values found in an image determines the exact number of clusters. Based on the contents of image, this number of clusters keeps on changing from image to image. After obtaining this number, it is given as an input to Gaussian Mixture Model (GMM) which clusters the image.GMM works with finite number of clusters and forms mixture of various spectral densities contained in that image. The proposed method facilitates GMM to adapt itself according to the changing number of clusters. Therefore, the proposed model along with the inclusion of GMM, is named as Adaptive Finite Gaussian Mixture Model (AFGMM).  The clustering performance of AFGMM is evaluated through Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). Both of these performance measuring methods confirmed that exact number of clusters is essentially important for reliably analyzing an image.

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Ahmed, M. M., & Mohamad, D. B. (2008). Segmentation of brain MR images for tumor extraction by combining kmeans clustering and perona-malik anisotropic diffusion model. International Journal of Image Processing, 2(1), 27-34.

Razak, Z., Zulkiflee, K., Noor, N. M., Salleh, R., & Yaacob, M. (2009). Off-line handwritten Jawi character segmentation using histogram normalization and sliding window approach for hardware implementation. Malaysian Journal of Computer Science, 22(1), 34-43.

Benrabh, M., Bouroumi, A., & Hamdoun, A. (2005). A fuzzy validity-guided procedure for cluster detection. Malaysian Journal of Computer Science, 18(1), 31-39.

Jung, C., Kim, C., Chae, S. W., & Oh, S. (2010). Unsupervised segmentation of overlapped nuclei using Bayesian classification. IEEE Transactions on Biomedical Engineering, 57(12), 2825-2832.

Yi, W., Yao, M., & Jiang, Z. (2006, November). Fuzzy particle swarm optimization clustering and its application to image clustering. In Pacific-Rim Conference on Multimedia (pp. 459-467). Springer, Berlin, Heidelberg.

Patil, R. V., & Jondhale, K. C. (2010, July). Edge based technique to estimate number of clusters in k-means color image segmentation. In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on (Vol. 2, pp. 117-121). IEEE.

Le Capitaine, H., & Frelicot, C. (2010, August). On selecting an optimal number of clusters for color image segmentation. In Pattern Recognition (ICPR), 2010 20th International Conference on (pp. 3388-3391). IEEE.

Erilli, N. A., Yolcu, U., Eğrioğlu, E., Aladağ, Ç. H., & Öner, Y. (2011). Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks. Expert Systems with Applications, 38(3), 2248-2252.

Wang, L., Leckie, C., Ramamohanarao, K., & Bezdek, J. (2009). Automatically determining the number of clusters in unlabeled data sets. IEEE Transactions on knowledge and Data Engineering, 21(3), 335-350.

Rosenberger, C., & Chehdi, K. (2000). Unsupervised clustering method with optimal estimation of the number of clusters: Application to image segmentation. In Pattern Recognition, 2000. Proceedings. 15th International Conference on (Vol. 1, pp. 656-659). IEEE.

Chen, Y. H., Ho, Y. W., Wu, C. H., & Lai, C. C. (2009, May). Aerial image clustering using genetic algorithm. In Computational Intelligence for Measurement Systems and Applications, 2009. CIMSA'09. IEEE International Conference on (pp. 42-45). IEEE.

Ali, L., Hussain, A., Li, J., Shah, A., Sudhakr, U., Mahmud, M., ... & Rajak, M. (2014, December). Intelligent image processing techniques for cancer progression detection, recognition and prediction in the human liver. In Computational Intelligence in Healthcare and e-health (CICARE), 2014 IEEE Symposium on(pp. 25-31). IEEE.

Beale, M. H., Hagan, M. T., & Demuth, H. B. (2012). Neural network toolbox™ user’s guide. In R2012a, The MathWorks, Inc., 3 Apple Hill Drive Natick, MA 01760-2098,, www. mathworks. com.

Leung, S., Liang, G., Solna, K., & Zhao, H. (2009). Expectation-maximization algorithm with local adaptivity. SIAM journal on imaging sciences, 2(3), 834-857.

Tran, T. N., Wehrens, R., & Buydens, L. M. (2005). Clustering multispectral images: a tutorial. Chemometrics and Intelligent Laboratory Systems, 77(1-2), 3-17.

Rocha, A., & Room, C. D. T. MO444/MC886.

Naidu, V. P. S., & Raol, J. R. (2008). Pixel-level image fusion using wavelets and principal component analysis. Defence Science Journal, 58(3), 338.

Srivisal, C., & Lursinsap, C. (2009, April). Predicting Number of Unsupervised Clusters by Supervised Function. In 2009 International Joint Conference on Computational Sciences and Optimization (pp. 726-730). IEEE.

Vinh, N. X., & Epps, J. (2009, June). A novel approach for automatic number of clusters detection in microarray data based on consensus clustering. In Bioinformatics and BioEngineering, 2009. BIBE'09. Ninth IEEE International Conference on (pp. 84-91). IEEE.

Langan, D. A., Modestino, J. W., & Zhang, J. (1998). Cluster validation for unsupervised stochastic model-based image segmentation. IEEE Transactions on Image Processing, 7(2), 180-195.

Ahmed, M. M., Zain, J. M., & Rana, M. T. A. (2012, November). Context Independent Expectation Maximization Algorithm for Segmentation of Brain MR Images. In Advanced Computer Science Applications and Technologies (ACSAT), 2012 International Conference on (pp. 436-441). IEEE.

DOI: http://dx.doi.org/10.21015/vtse.v14i1.540


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