Kraleti Srinivasa Rao

Work place: Department of Statistics, Andhra University, Visakhapatnam



Research Interests: Data Structures and Algorithms, Data Mining, Image Processing, Systems Architecture


Dr. K. Srinivasa Rao is professor and chairman of P.G. Board of studies, Andhra University. He published 108 papers in reputed national and international journals. He guided 30 students for their Ph.D. degrees in 6 disciplines. He is the chief editor of journal of ISPS. He is the fellow of AP Academy of sciences. His current research interests are Data-mining, Stostatic modeling, Image Processing and Statistical Signal Processing.

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:, 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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Performance Evaluation on the Effect of Combining DCT and LBP on Face Recognition System

By Dasari Haritha Kraleti Srinivasa Rao Chittipotula Satyanarayana

DOI:, Pub. Date: 8 Nov. 2012

In this paper, we introduce a face recognition algorithm based on doubly truncated multivariate Gaussian mixture model with Discrete Cosine Transform (DCT) and Local binary pattern (LBP). Here, the input face image is transformed to the local binary pattern domain. The obtained local binary pattern image is divided into non-overlapping blocks. Then from each block the DCT coefficients are computed and feature vector is extracted. Assigning that the feature vector follows a doubly truncated multivariate Gaussian mixture distribution, the face image is modelled. By using the Expectation-Maximization algorithm the model parameters are estimated. The initialization of the model parameters is done by using either K-means algorithm or hierarchical clustering algorithm and moment method of estimation. The face recognition system is developed with the likelihood function under Bayesian frame. The efficiency of the developed face recognition system is evaluated by conducting experimentation with JNTUK and Yale face image databases. The performance measures like half total error rate, recognition rates are computed along with plotting the ROC curves. A comparative study of the developed algorithm with some of the earlier existing algorithm revealed that this system perform better since, it utilizes local and global information of the face.

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