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Modified Image Thresholding Algorithm using Social Impact Theory Optimization (SITO)

by Harpreet Kaur, Shruti Mittal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 4
Year of Publication: 2014
Authors: Harpreet Kaur, Shruti Mittal
10.5120/18064-9001

Harpreet Kaur, Shruti Mittal . Modified Image Thresholding Algorithm using Social Impact Theory Optimization (SITO). International Journal of Computer Applications. 103, 4 ( October 2014), 29-32. DOI=10.5120/18064-9001

@article{ 10.5120/18064-9001,
author = { Harpreet Kaur, Shruti Mittal },
title = { Modified Image Thresholding Algorithm using Social Impact Theory Optimization (SITO) },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 4 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number4/18064-9001/ },
doi = { 10.5120/18064-9001 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:33:42.372796+05:30
%A Harpreet Kaur
%A Shruti Mittal
%T Modified Image Thresholding Algorithm using Social Impact Theory Optimization (SITO)
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 4
%P 29-32
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Thresholding is considered as pivotal tool for image segmentation [1]. The main aim of thresholding is to divide the pixels into different groups in a logical way [2]. One of the most suitable algorithm for thresholding is Social Impact Theory Based Optimization (SITO). Social Impact theory optimization algorithm has been considered as one of the important technique to find the better optimized results as it is based on human behavior . The cross entropy function works well in case of bi-level thresholding problem. However, if there is a need of the multi-thresholding in image processing application, a global and generic objective function is desired so that each threshold could be tested for its best performance statistically [6]. The maxima of the selected threshold are optimized by using the SITO algorithm based on maxima of sum of entropy ad standard deviation. The results are compared with negative selection algorithm (NSA) which is artificial intelligence (AI) technique, maximum entropy algorithm and OTSU algorithm. The performance measures i. e. Standard Deviation, Entropy, MSE and PSNR prove the improvements of SITO based thresholding.

References
  1. Prasant Kumar Mahapatra,Mandeep Kaur,Spardha Sethi,Rishabh Thareja,Amod Kumar,Swapna Devi,"Improved thresholding based on negative selection algorithm(NSA)," –Springer 2013.
  2. Yi Hong and Hanli Wang, "Image thresholding based on Random spatial sampling and Majority voting," Dept. of Comput. Sci. ,Vol. 2, IEEE, 2010.
  3. M Macas, "Social Impact Theory Based Optimization," IEEE Transactions on Soft Computing, Vol. 23, 2001.
  4. Ying Zhuge,Jayaram K. Udupa,Punam K. Saha, "Vectorial scale-based fuzzy-connected image segmentation,"Medical Image Processing Grroup,USA 2006.
  5. Guang Yang, "Study on Statistics Iterative Thresholding Segmentation Based on Aviation Image," IEEE, Vol. 2, 2007.
  6. Kamal Hammouche,Moussa Diaf,Patrick Siarry, "A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation,"Science Direct, 2007.
  7. Kezong Tanga,Xiaojing Yuan,Tingkai Sun ,Jingyu Yang,Shang Gao, "An improved scheme for minimum cross entropy threshold selection based on genetic algorithm," Science Direct, 2011.
  8. Chang,WangJ. ,Guo, "Survey and comparative analysis of entropy and relative entropy thresholding techniques," Vision,Image and Signal Processing,IEE Proceedings, Vol. 153, 2006.
  9. Minh Luan Nguyen, "Rice leaf detection with genetic programming," Computer and Information Technology, 2008
  10. Soham Sarkar,Gyana Ranjan Patra, and Swagtam Das, "A differential Evolution Based Approach for Multilevel Image Segmentation Using Minimum Cross Entropy Thresholding," Springer Berlin Heidelberg, 2011.
  11. Azami, H. , "Segmentation of medical images based on hierarchical evolutionary and bee algorithms," IEEE, Vol. 93, 2013.
  12. Suhre,A. ;Kose,K;Cetin,A. E. , "Image Compression using a histogram-based color transform," IEEE Trans. Evol. Comput. , 9(1), pp. 61-73, 2010.
  13. M. Tripathy, and S. Mishra, "Bacterial foraging based solution to optimize both real power and voltage stability limit," IEEE Trans. Power Syst. , 22(1), pp. 240-248, 2007.
  14. W. Lin, and P. X. Liu, "Hammerstein model identification based on bacterial foraging," IEE Electronics Letters, 42(23), pp. 1332-1334, 2006.
  15. P. K. Sahoo, S. Soltani, and A. K. C. Wong, "A survey of thresholding techniques,"Computer Vision, Graphics and Image Processing, vol. 41(2), pp. 233-260, 1988.
  16. C. A. Glasbey, "An analysis of histogram based thresholding algorithms," CVGIP: Graphical Models and Image Processing, Vol. 55, pp. 532-537, 1993.
Index Terms

Computer Science
Information Sciences

Keywords

SITO Thresholding NSA MSE PSNR

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