Performance Evaluation of Image Segmentation Method based on Doubly Truncated Generalized Laplace Mixture Model and Hierarchical Clustering

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T.Jyothirmayi 1,* K Srinivasa Rao 2 P.Srinivasa Rao 3 Ch.Satyanarayana 4

1. Department of Computer Science and Engineering, GITAM University, Visakhapatnam

2. Department of Statistics, Andhra University, Visakhapatnam

3. Department of Computer Science and Systems Engineering, Andhra University, Visakhapatnam

4. Department of Computer Science and Engineering, JNTU-Kakinada

* Corresponding author.


Received: 23 Sep. 2016 / Revised: 28 Oct. 2016 / Accepted: 7 Dec. 2016 / Published: 8 Jan. 2017

Index Terms

Image segmentation, Generalized Laplace Mixture Model, doubly truncated generalized Laplace Mixture Model, EM algorithm


The present paper aims at performance evaluation of Doubly Truncated Generalized Laplace Mixture Model and Hierarchical clustering (DTGLMM-H) for image analysis concerned to various practical applications like security, surveillance, medical diagnostics and other areas. Among the many algorithms designed and developed for image segmentation the dominance of Gaussian Mixture Model (GMM) has been predominant which has the major drawback of suiting to a particular kind of data. Therefore the present work aims at development of DTGLMM-H algorithm which can be suitable for wide variety of applications and data. Performance evaluation of the developed algorithm has been done through various measures like Probabilistic Rand index (PRI), Global Consistency Error (GCE) and Variation of Information (VOI). During the current work case studies for various different images having pixel intensities has been carried out and the obtained results indicate the superiority of the developed algorithm for improved image segmentation.

Cite This Paper

T.Jyothirmayi, K.Srinivasa Rao, P.Srinivasa Rao, Ch.Satyanarayana,"Performance Evaluation of Image Segmentation Method based on Doubly Truncated Generalized Laplace Mixture Model and Hierarchical Clustering", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.1, pp.41-49, 2017. DOI: 10.5815/ijigsp.2017.01.06


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