Image Outlier filtering (IOF) : A Machine learning based DWT optimization Approach

Image Outlier filtering (IOF) : A Machine learning based DWT optimization Approach

Dr. R.Sunitha

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Yugandhar Dasari

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Jawaharlal Nehru Technological University, Kakinada

Image Outlier filtering (IOF) : A Machine learning based DWT optimization Approach

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Abstract

In this paper an image outlier technique, which is a hybrid model called SVM regression based DWT optimization have been introduced. Outlier filtering of RGB image is using the DWT model such as Optimal-HAAR wavelet changeover (OHC), which optimized by the Least Square Support Vector Machine (LS-SVM) . The LS-SVM regression predicts hyper coefficients obtained by using QPSO model. The mathematical models are discussed in brief in this paper: (i) OHC which results in better performance and reduces the complexity resulting in (Optimized FHT). (ii) QPSO by replacing the least good particle with the new best obtained particle resulting in “Optimized Least Significant Particle based QPSO” (OLSP-QPSO). On comparing the proposed cross model of optimizing DWT by LS-SVM to perform oulier filtering with linear and nonlinear noise removal standards.

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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

Dr. R.Sunitha. 2012. \u201cImage Outlier filtering (IOF) : A Machine learning based DWT optimization Approach\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 12 (GJCST Volume 12 Issue F14).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

hybrid Machine Regression

Submission ReceivedFebruary 10, 2012
Peer Review Double Blind
Handling Editor
Accepted March 3, 2012
Published March 15, 2012

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v1.2

Issue date

November 24, 2012

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Article Matrices

Total Score: 107

Country: India

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

Authors: Dr. R.Sunitha, Yugandhar Dasari (PhD/Dr. count: 1)

View Count (all-time): 256

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Publish Date: 2012 11, Sat

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Total Views: 10189

Total Downloads: 2657

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