Image clustering comparison of two color segmentation techniques

The clustering research is regarding the area of data mining and implementation of the clustering algorithms. The image clustering is major part of data mining where study about how to binds the similar data together in a cluster and show the meaningful data. There are many algorithm for analysing c...

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Main Author: Subramaniam, Kavitha Pichaiyan
Format: Thesis
Language:English
English
Published: 2010
Subjects:
Online Access:http://eprints.utem.edu.my/id/eprint/12808/1/Image_clustering_comparison_of_two_color_segmentation_techniques24_pages.pdf
http://eprints.utem.edu.my/id/eprint/12808/3/Image%20clustering%20comparison%20of%20two%20color%20segmentation%20techniques.pdf
http://eprints.utem.edu.my/id/eprint/12808/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=63009
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spelling my.utem.eprints.128082022-11-11T08:47:12Z http://eprints.utem.edu.my/id/eprint/12808/ Image clustering comparison of two color segmentation techniques Subramaniam, Kavitha Pichaiyan T Technology (General) TA Engineering (General). Civil engineering (General) The clustering research is regarding the area of data mining and implementation of the clustering algorithms. The image clustering is major part of data mining where study about how to binds the similar data together in a cluster and show the meaningful data. There are many algorithm for analysing clustering each having its own method to do clustering. This clustering technique increasingly common and has yield many insights into segmentation factors, would effect image functioning and performance. The enormous researches going on extract image with background subtraction. We focus on the outlier detection and background subtraction on image. This project proposed a two color segmentation techniques such as K-means and Fuzzy C-means clustering algorithm that are accurately segment the desired images, which have the same color as the pre-selected pixels with background subtraction. In the software development testing we examine image based clustering, as we can used clustering by distance base, by pixel (red, green, blue) value etc., The problem is solved by region based method which is based on connect component and background detection techniques. The appropriate Java codes are developed for solve this task. The developed patterns are applied in the field of real-time analysis. Finally, the algorithm found, which would solve the image segmentation problem. 2010 Thesis NonPeerReviewed text en http://eprints.utem.edu.my/id/eprint/12808/1/Image_clustering_comparison_of_two_color_segmentation_techniques24_pages.pdf text en http://eprints.utem.edu.my/id/eprint/12808/3/Image%20clustering%20comparison%20of%20two%20color%20segmentation%20techniques.pdf Subramaniam, Kavitha Pichaiyan (2010) Image clustering comparison of two color segmentation techniques. Masters thesis, Universiti Teknikal Malaysia Melaka. https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=63009
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
Subramaniam, Kavitha Pichaiyan
Image clustering comparison of two color segmentation techniques
description The clustering research is regarding the area of data mining and implementation of the clustering algorithms. The image clustering is major part of data mining where study about how to binds the similar data together in a cluster and show the meaningful data. There are many algorithm for analysing clustering each having its own method to do clustering. This clustering technique increasingly common and has yield many insights into segmentation factors, would effect image functioning and performance. The enormous researches going on extract image with background subtraction. We focus on the outlier detection and background subtraction on image. This project proposed a two color segmentation techniques such as K-means and Fuzzy C-means clustering algorithm that are accurately segment the desired images, which have the same color as the pre-selected pixels with background subtraction. In the software development testing we examine image based clustering, as we can used clustering by distance base, by pixel (red, green, blue) value etc., The problem is solved by region based method which is based on connect component and background detection techniques. The appropriate Java codes are developed for solve this task. The developed patterns are applied in the field of real-time analysis. Finally, the algorithm found, which would solve the image segmentation problem.
format Thesis
author Subramaniam, Kavitha Pichaiyan
author_facet Subramaniam, Kavitha Pichaiyan
author_sort Subramaniam, Kavitha Pichaiyan
title Image clustering comparison of two color segmentation techniques
title_short Image clustering comparison of two color segmentation techniques
title_full Image clustering comparison of two color segmentation techniques
title_fullStr Image clustering comparison of two color segmentation techniques
title_full_unstemmed Image clustering comparison of two color segmentation techniques
title_sort image clustering comparison of two color segmentation techniques
publishDate 2010
url http://eprints.utem.edu.my/id/eprint/12808/1/Image_clustering_comparison_of_two_color_segmentation_techniques24_pages.pdf
http://eprints.utem.edu.my/id/eprint/12808/3/Image%20clustering%20comparison%20of%20two%20color%20segmentation%20techniques.pdf
http://eprints.utem.edu.my/id/eprint/12808/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=63009
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score 13.160551