Image Segmentation using Rough Set based Fuzzy K-Means Algorithm

Image Segmentation using Rough Set based Fuzzy K-Means Algorithm

ev reddy PhD

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E. Venkateswara Reddy

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Dr. E.S.Reddy

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Acharya Nagarjuna University

Image Segmentation using Rough Set based Fuzzy K-Means Algorithm

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Abstract

Image segmentation is critical for many computer vision and information retrieval systems, and has received significant attention from industry and academia over last three decades. Despite notable advances in the area, there is no standard technique for selecting a segmentation algorithm to use in a particular application, nor even is there an agreed upon means of comparing the performance of one method with another. This paper, explores Rough-Fuzzy K-means (RFKM) algorithm, a new intelligent technique used to discover data dependencies, data reduction, approximate set classification, and rule induction from image databases. Rough sets offer an effective approach of managing uncertainties and also used for image segmentation, feature identification, dimensionality reduction, and pattern classification. The proposed algorithm is based on a modified K-means clustering using rough set theory (RFKM) for image segmentation, which is further divided into two parts. Primarily the cluster centers are determined and then in the next phase they are reduced using Rough set theory (RST). K-means clustering algorithm is then applied on the reduced and optimized set of cluster centers with the purpose of segmentation of the images. The existing clustering algorithms require initialization of cluster centers whereas the proposed scheme does not require any such prior information to partition the exact regions. Experimental results show that the proposed method perform well and improve the segmentation results in the vague areas of the image.

References

12 Cites in Article

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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

ev reddy. 2013. \u201cImage Segmentation using Rough Set based Fuzzy K-Means Algorithm\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 13 (GJCST Volume 13 Issue F6).

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GJCST Volume 13 Issue F6

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

K-means algorithm RGB images rough set uncertain images

Submission ReceivedDecember 8, 2012
Peer Review Double Blind
Handling Editor
Accepted January 5, 2013
Published January 15, 2013

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v1.2

Issue date

August 10, 2013

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Article Matrices

Total Score: 107

Country: Unknown

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

Authors: E. Venkateswara Reddy, Dr. E.S.Reddy (PhD/Dr. count: 1)

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Total Downloads: 2457

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