International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.8, No.3, Mar. 2016
Studies on Texture Segmentation Using D-Dimensional Generalized Gaussian Distribution integrated with Hierarchical Clustering
Full Text (PDF, 526KB), PP.45-54
Texture deals with the visual properties of an image. Texture analysis plays a dominant role for image segmentation. In texture segmentation, model based methods are superior to model free methods with respect to segmentation methods. This paper addresses the application of multivariate generalized Gaussian mixture probability model for segmenting the texture of an image integrating with hierarchical clustering. Here the feature vector associated with the texture is derived through DCT coefficients of the image blocks. The model parameters are estimated using EM algorithm. The initialization of model parameters is done through hierarchical clustering algorithm and moment method of estimation. The texture segmentation algorithm is developed using component maximum likelihood under Bayesian frame. The performance of the proposed algorithm is carried through experimentation on five image textures selected randomly from the Brodatz texture database. The texture segmentation performance measures such as GCE, PRI and VOI have revealed that this method outperform over the existing methods of texture segmentation using Gaussian mixture model. This is also supported by computing confusion matrix, accuracy, specificity, sensitivity and F-measure.
Cite This Paper
K. Naveen Kumar, K. Srinivasa Rao, Y.Srinivas, Ch. Satyanarayana,"Studies on Texture Segmentation Using D-Dimensional Generalized Gaussian Distribution integrated with Hierarchical Clustering", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.3, pp.45-54, 2016.DOI: 10.5815/ijigsp.2016.03.06
Amadasun M., King R., (1989), "Textural features corresponding to textural properties," IEEE Transactions on Systems, Man and Cybernetics, Vol. 19(5), pp.1264-1274.
Haralick, R.M., (1979),"Statistical and Structural Approaches to Texture," Proceedings of the IEEE Computer Society, Vol. 67, pp. 786-804.
Lu C., Chung P. and Chen C. (1997), "Unsupervised texture segmentation via wavelet Transform", IEEE Trans. on Pattern Recognition, Vol.30(5),pp.729-742.
S. Krishnamachari and R. Chellappa (1997), "Multiresolution Gauss-Markov Random field models for Texture segmentation", IEEE Trans. Image Processing,Vol.2,pp.171-179.
Kostas Haris, Serafim N. Efstratiadis, Nicos Maglaveras, and Aggelos K. Katsaggelos (1998), "Hybrid Image Segmentation Using Watersheds and Fast Region Merging", IEEE Transactions on Image Processing, Vol.7(12).
Y. Zhang, M. Brady, and S. Smith,(2001), "Segmentation of Brain MRI Images through a Hidden Markov Random Field model and EM algorithm", IEEE Trans. Med. Imaging., Vol.20(1),pp.45-57.
Vaijinath V. Bhosle, Vrushsen P.Pawar (2013), "Texture Segmentation: Different Methods", International Journal of Soft Computing and Engineering, Vol.3(5),pp.69-74.
Pal S.K and Pal N.R. (1993), "A review on Image Segmentation Techniques", IEEE Trans. on Pattern Recognition, Volume 26(9), pp.1277-1294.
Haim Permuter et al. (2006), "A study of Gaussian mixture models of color and texture features for image classification and segmentation", The Journal of the Pattern Recognition Society, Vol.39, pp.695-706.
M. N. Thanh, Q.M.J.Wu (2011), "Gaussian mixture model based spatial neighbourhood relationships for pixel labeling problem", IEEE Trans. Syst. Man Cybern.,Vol.99, pp.1-10.
Paul D., Mc Nicholas (2011), "On Model-Based Clustering, Classification, and Discriminant Analysis", Journal of Iranian Statistical Society, Journal of Iranian Statistical Society.
G. V. S. Raj Kumar, K. Srinivasa Rao and P. Srinivasa Rao (2011), "Image segmentation and Retrievals based on finite doubly truncated bivariate Gaussain mixture model and K-means", International Journal of Computer Applications, Volume 25(5), pp.5-13.
Yu-Len Huang (2005), "A fast method for textural analysis of DCT-based image", Journal of Information Science and Engineering, Volume 21, pp.181-194.
Srinivas Y. and Srinivasa Rao K. (2007), "Unsupervised image segmentation using finite doubly truncated Gaussian mixture model and Hierarchical clustering", Journal of Current Science , Vol.93(4), pp.507-514.
P. Brodatz,(1966)"Texture: a photographic album for artists and designers,", Dover, New York,USA(http.://sipi.usc.edu/database/database.php?volume=textures).
M.S.Allili and Nizar Bougila (2008), "Finite generalized Gaussian mixture modeling and applications to image and video foreground segmentation", Journal of Electronic Imaging, Volume 17(13), pp.05-13
C. Lu, P. Chung and C. Chen(1997), "Unsupervised Texture Segmentation via Wavelet Transform", IEEE Trans. on Pattern Recognition, Volume 30(5), pp. 729-742.
T. P. Weldon, W.E. Higgins (1996), "Design of multiple Gabor filters for texture segmentation", Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, pp.2243-2246.
Rao K.R. and Yip P. (1990), "Discrete Cosine Transform – Algorithms, Advantages, Applications", Academic press, New York, USA.
Gonzales R.C., Woods R.E.,(1992), "Digital Image Processing", Addison –Wesley.
Mclanchlan G. and Peel D.(2000), " The EM Algorithm for Parameter Estimations", John Wileyand Sons, New York, USA.
Shaoquan YU, Anyi Zhang, Hongwei LI (2012), "A Review on estimating the Shape Parameters of Generalized Gaussian Distribution", Journal of Information Systems, Volume 8(21),pp.9055-9064.
Jeff A. Bilmes (1977), "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models", Intl. Computer Science Institute, Berkeley.
Unnikrishnan R., Pantofaru C., and Hernbert M. (2007),"Toward objective evaluation of image segmentation algorithms," IEEE Trans. Pattern Annl. & Mach.Intell, Vol.29(6), pp. 929-944.
M. Meila (2007), "Comparing clusterings- an information based distance", Journal of Multivariate Analysis, Vol. 98, pp. 873 - 895.
D. Martin, C. Fowlkes, D. Tal and J. Malik (2001), "A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics", Proc. 8th Int'l Conf. Computer Vision, Vol. 2, pp. 416-423.
Powers, David M.W. (2011), "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation", Journal of Machine Learning Technologies, Vol.2(1), pp.37-63.