K. Naveen Kumar

Work place: Department of IT, GIT, GITAM University, Visakhapatnam

E-mail: nkumarkuppili@gmail.com


Research Interests: Image Processing, Network Security, Network Architecture, Computer systems and computational processes


Mr. K. Naveen Kumar is presently Assistant Professor in the department of Information Technology, GIT, GITAM University, Visakhapatnam. He presented several research papers in national and international conferences/seminars and journals of good repute. He guided several students for getting their M.Tech Degrees in field of Computer Science Engineering. His current research interests include image processing and network security

Author Articles
Studies on Texture Segmentation Using D-Dimensional Generalized Gaussian Distribution integrated with Hierarchical Clustering

By K. Naveen Kumar Kraleti Srinivasa Rao Y.Srinivas Ch. Satyanarayana

DOI: https://doi.org/10.5815/ijigsp.2016.03.06, Pub. Date: 8 Mar. 2016

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.

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