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A Comparative Study on Clustering Algorithms using Image Data

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Vikas Tondar, Pramod S. Nair
10.5120/ijca2016908071

Vikas Tondar and Pramod S Nair. Article: A Comparative Study on Clustering Algorithms using Image Data. International Journal of Computer Applications 133(17):28-31, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Vikas Tondar and Pramod S. Nair},
	title = {Article: A Comparative Study on Clustering Algorithms using Image Data},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {17},
	pages = {28-31},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Analyzing of image called Segmentation .It is an important concept to viewing and analyzing different type’s images and solving a wide range of problems in image. Clustering algorithm and technique for classifying usage image data and the process of analyze image data from dissimilar perception and abbreviation it into valuable information, this information can be use to increase proceeds, cuts costs, or Time complexity. There is different type of algorithms for image data and clustering such as (FCM) fuzzy c-means clustering algorithms, SFCM (Spatial fuzzy c-means clustering), K-Means, and PSOFCM (particle swarm optimization incorporative fuzzy c-means clustering) .The selection between the predictive classifier is extremely important. Fuzzy algorithms based on initial cluster selection without noise data. PSOFCM and SFCM approaches shows better segmentation results can be obtained in noise.

PSOFCM and SFCM approaches shows how better image segmentation of results can be obtained. Image clustering and its applications are used in human image i.e. Medical image segmentation used for detection of Brain images, tumor and more. The result obtained through Particle swarm optimization (PSO), yields better detected image and time complexity compared to FCM and SFCM.

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Keywords

FCM, Particle swarm optimization based FCM, spatial information based FCM.