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Ant Colony Optimization with the Fusion of Adaptive K-means and Gaussian Second Derivative for Image Segmentation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 134 - Number 3 |
| Year of Publication: 2016 |
| Authors: Pragya Sharma, Unmukh Datta |
10.5120/ijca2016907890
|
Pragya Sharma, Unmukh Datta . Ant Colony Optimization with the Fusion of Adaptive K-means and Gaussian Second Derivative for Image Segmentation. International Journal of Computer Applications. 134, 3 ( January 2016), 44-47. DOI=10.5120/ijca2016907890
Abstract
In this study, proposed an ant colony optimization (ACO) with the fusion of adaptive k-means and Gaussian second derivative for image segmentation. With the use of two algorithms will enhance the segmentation accuracy and speed up algorithm convergence. In the Gaussian second derivative, it is used for enhancing edges of an image because some information loses in the previous algorithm. The experimental process proved that a new hybrid clustering algorithm is more efficient than previous algorithms. Principally, this algorithm has better results in image segmentation. The proposed method can get profit of the K-means clustering for image segmentation in the aspects of less execution time. Also, it can get the benefits of ACO in the aspects of f-measure accuracy.
References
- Ina Singh, Asst. Prof. Neelakshi Gupta ," Segmentation of Liver using Hybrid K-means Clustering and Level Set ",International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 8, August 2015, pp:742-746
- Varshali Jaiswal and Aruna Tiwari,” A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach”, IOSR Journal of Computer Engineering (IOSR-JCE), 8727Volume 15, Issue 3 (Nov. - Dec. 2013), PP 71-78
- Vrushali D. Mendhule , Gaurav Soni, Alesh Sharma,” Interactive Image Segmentation Using Combined MRF and Ant Colony Optimization”, International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 4 Issue 6 June 2015, Page No. 12281-12288
- Jue Lu and Rongqiang Hu,"A new hybrid clustering algorithm based on K-means and ant colony algorithm",Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013), pp:1718-1721.
- Vahidsolemani, FarnoHeidariVincheh,“ Improving Ant Colony Optimization for Brain MRI Segmentation andBrain Tumor Diagnosis”, IEEE 2013, 978-1-4673-6206-1/13.
- Ms. Aparna K and Dr. Mydhili K Nair,"A Detailed Study and Analysis of different Partitional Data Clustering Techniques",International Journal of Innovative Research in Science, Engineering and Technology,Vol. 3, Issue 1, January 2014, pp: 8353-8359.
- Dharmendra K. Roy and Lokesh Sharma, “ Genetic K-Means Clustering algorithm for mixed and categorical data sets”, IJAIA 2010.
- N. Shi, “Research on K-Means Clustering Algorithms: An improved K-Means Algorithms”, Intelligent Information Technology and Security Informatics, 63-67, IEEE 2010.
- Ina Singh, Asst. Prof. Neelakshi Gupta “ Segmentation of Liver using Hybrid K-means Clustering and Level Set” International Journal of Advanced Research in Computer Science and Software Engineering 5(8), August- 2015, pp. 742-746 © 2015, IJARCSSE
- Bhagwati Charan Patel, Dr. G.R.Sinha “An Adaptive K-means Clustering Algorithm for Breast Image Segmentation” International Journal of Computer Applications (0975 – 8887) Volume 10– N.4, November 2010
- A. M. Khan, Ravi. S,” Image Segmentation Methods: A Comparative Study”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-4, September 2013, pp:84-92.
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