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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.
Dr. R.Sunitha, Yugandhar Dasari. 2012. "Image Outlier filtering (IOF) : A Machine learning based DWT optimization Approach". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 12 (GJCST Volume 12 Issue F14).
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
v1.2
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Total Score: 147
Country: India
Subject: Global Journal of Computer Science and Technology
Authors: Dr. R.Sunitha, Yugandhar Dasari (PhD/Dr. count: 1)
View Count (all-time): 417
Total Views (Real + Logic): 2454
Total Downloads (simulated): 206
Publish Date: 2012 03, Thu
Monthly Totals (Real + Logic):
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