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

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Dr. R.Sunitha
Dr. R.Sunitha
2
Yugandhar Dasari
Yugandhar Dasari
1 Aditya Institute of Technology and Management, Tekkali.

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GJCST Volume 12 Issue F14

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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.

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.

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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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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v1.2

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November 24, 2012

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English

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

Dr. R.Sunitha
Dr. R.Sunitha Aditya Institute of Technology and Management, Tekkali.
Yugandhar Dasari
Yugandhar Dasari

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