Image Segmentation for Animal Images using Finite Mixture of Pearson Type VI Distribution

Image Segmentation for Animal Images using Finite Mixture of Pearson Type VI Distribution

chandoo

Contact

K. Srinivasa Rao

Contact

P. Chandra Sekhar

Contact

P. Srinivasa Rao

Contact

GITAM University

Image Segmentation for Animal Images using Finite Mixture of Pearson Type VI Distribution

Article Fingerprint

ReserarchID

CSTGVZCPO9

Image Segmentation for Animal Images using Finite Mixture of Pearson Type VI Distribution Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

Image Segmentation is one of the significant tool for analyzing images, the feature vector of the images are different for different types of images. In remote sensing, Environmental ecological systems, forest studies, conservation of rare animals, the animal images are more important. In this paper we developed and analyze an image segmentation algorithm using mixture of Pearson Type VI Distribution. The Pearsonian Type VI Distribution will characterize the image regions of animal images. The appropriateness Pearsonian Type VI distribution for the pixel intensities of image region in animal images is carried by fitting Pearsonian Type VI Distribution to set of animal images taken from Berkeley image data set. The image segmentation algorithm is developed using EM algorithm for estimating the parameters of the model and maximum likelihood for image component under Bayesian framework. For fast convergence of EM algorithm the initial estimates of the model parameters are obtained by dividing the whole image into K image regions using K-means and Hierarchical clustering algorithm and utilizing the moment method of estimates. The performance of proposed algorithm is studied by conducting an experiment with set of animal images and computing image quality metrics such as PRI, GCE and VOI.

References

21 Cites in Article

Reference Format

Cheng (2001). Color Image Segmentation: Advances and Prospects.
M Eskicioglu,P Fisher (1995). Image Quality Measures and their Performance.
K Gvs Rajkumar,Srinivasa,And Rao,Srinivasa Rao- (2011). Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and K-Means.
Jahne (1995). A Practical Hand Book on Image segmentation for Scientific Applications.
P Kelly (1998). Statistical approach to Xray CT imaging and its applications in image analysis.
T Lei (2003). Performance Evaluation of Finite Normal Mixture Model -Based Image Segmentation Techniques.
Mantas Paulinas,Andrius Usinskas (2007). A survey of genentic algorithms applications for image enhancement and segmentation.
D Marr,E Hildreth (1980). Theory of edge detection.
M Seshashayee,K Srinivasa,Ch Rao,P Satyanarayana And,Srinivasa Rao- (2011). Image Segmentation Based on a Finite Generalized New Symmetric Mixture Model with K -Means.
M Seshashayee,K Srinivasa,Ch Rao,P Satyanarayana And,Srinivasa Rao- (2011). Studies on Image Segmentation method Based on a New Symmetric Mixture Model with K -Means.
K Kumar,K Rao,Y Srinivas,C Satyanarayana (2010). Studies on Texture Segmentation Using D-Dimensional Generalized Gaussian Distribution integrated with Hierarchical Clustering.
Srinivas Yerramalle,And Rao (2007). Unsupervised image classification using finite truncated Gaussian mixture model.
H Rose,Turi (2001). Cluster Based Image Segmentation.
G Mclanchan,D Peel (2000). Advanced organic chemistry Third edition), by G. W. Wheland. Pp. xi + 871. John Wiley & Sons Inc., New York; John Wiley & Sons Ltd, London. 1960. E7 net.
Meila (2005). Comparing clusterings.
N Nasios,A Bors (2006). Variational learning for Gaussian Mixtures.
Nikhil Pal,Sankar Pal (1993). A review on image segmentation techniques.
P Prasad Reddy,K Rao And,Srinivas Yerramalle,- (2007). supervised image segmentation using finite Generalized Gaussian mixture model with EM & K-Means algorithm.
P Prasad Reddy,K Rao And,Srinivas Yerramalle,- (2007). supervised image segmentation using finite Generalized Gaussian mixture model with EM & K-Means algorithm.
Y Srinivas (2007). Unsupervised Image Segmentation based on Finite Doubly Truncated Gaussian Mixture model with K-Means algorithm.
Shital Raut,M Raghuwanshi,R Dharaskar,Adarsh Raut (2009). Image Segmentation – A State-Of-Art Survey for Prediction.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

chandoo. 2014. \u201cImage Segmentation for Animal Images using Finite Mixture of Pearson Type VI Distribution\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 14 (GJCST Volume 14 Issue F3).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 14 Issue F3

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

EM algorithm Image Segmentation Performance Measures type VI pearsonian

Submission ReceivedDecember 10, 2013
Peer Review Double Blind
Handling Editor
Accepted January 2, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

August 21, 2014

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 103

Country: India

Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision

Authors: K. Srinivasa Rao, P. Chandra Sekhar, P. Srinivasa Rao (PhD/Dr. count: 0)

View Count (all-time): 266

Total Views (Real + Logic): 8700

Total Downloads (simulated): 2278

Publish Date: 2014 08, Thu

Monthly Totals (Real + Logic):

Month 1: 25 views
Month 2: 35 views
Month 3: 25 views
Month 4: 42 views
Month 5: 47 views
Month 6: 37 views
Month 7: 28 views
Month 8: 23 views
Month 9: 36 views
Month 10: 37 views
Month 11: 31 views
Month 12: 33 views
Month 13: 24 views
Month 14: 37 views
Month 15: 36 views
Month 16: 33 views
Month 17: 33 views
Month 18: 26 views
Month 19: 32 views
Month 20: 31 views
Month 21: 31 views
Month 22: 32 views
Month 23: 27 views
Month 24: 26 views
Month 25: 21 views
Month 26: 17 views
Month 27: 36 views
Month 28: 26 views
Month 29: 25 views
Month 30: 16 views
Month 31: 15 views
Month 32: 43 views
Month 33: 15 views
Month 34: 36 views
Month 35: 37 views
Month 36: 25 views
Month 37: 22 views
Month 38: 36 views
Month 39: 26 views
Month 40: 19 views
Month 41: 23 views
Month 42: 25 views
Month 43: 26 views
Month 44: 25 views
Month 45: 19 views
Month 46: 32 views
Month 47: 32 views
Month 48: 40 views
Month 49: 41 views
Month 50: 16 views
Month 51: 37 views
Month 52: 12 views
Month 53: 27 views
Month 54: 33 views
Month 55: 35 views
Month 56: 31 views
Month 57: 22 views
Month 58: 36 views
Month 59: 30 views
Month 60: 50 views
Month 61: 28 views
Month 62: 18 views
Month 63: 39 views
Month 64: 43 views
Month 65: 44 views
Month 66: 29 views
Month 67: 36 views
Month 68: 36 views
Month 69: 22 views
Month 70: 31 views
Month 71: 31 views
Month 72: 18 views
Month 73: 25 views
Month 74: 25 views
Month 75: 38 views
Month 76: 23 views
Month 77: 49 views
Month 78: 35 views
Month 79: 24 views
Month 80: 43 views
Month 81: 27 views
Month 82: 23 views
Month 83: 40 views
Month 84: 28 views
Month 85: 22 views
Month 86: 24 views
Month 87: 42 views
Month 88: 14 views
Month 89: 26 views
Month 90: 39 views
Month 91: 40 views
Month 92: 26 views
Month 93: 34 views
Month 94: 36 views
Month 95: 37 views
Month 96: 41 views
Month 97: 32 views
Month 98: 27 views
Month 99: 33 views
Month 100: 42 views
Month 101: 28 views
Month 102: 33 views
Month 103: 40 views
Month 104: 30 views
Month 105: 36 views
Month 106: 27 views
Month 107: 21 views
Month 108: 33 views
Month 109: 48 views
Month 110: 29 views
Month 111: 34 views
Month 112: 42 views
Month 113: 28 views
Month 114: 32 views
Month 115: 28 views
Month 116: 42 views
Month 117: 30 views
Month 118: 39 views
Month 119: 33 views
Month 120: 36 views
Month 121: 28 views
Month 122: 28 views
Month 123: 14 views
Month 124: 33 views
Month 125: 24 views
Month 126: 41 views
Month 127: 19 views
Month 128: 25 views
Month 129: 22 views
Month 130: 22 views
Month 131: 44 views
Month 132: 18 views
Month 133: 30 views
Month 134: 27 views
Month 135: 48 views
Month 136: 26 views
Month 137: 27 views
Month 138: 17 views
Month 139: 55 views
Month 140: 46 views
Month 141: 54 views

Total Views: 8700

Total Downloads: 2278

2026 Trends

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]