Extractive Support Vector Algorithm on Support Vector Machines for Image Restoration

• Autorzy: Yao C-C. , Yu P-T. , Hung R-W.
• Języki publikacji: EN

Abstrakty

EN

The major problem of SVMs is the dependence of the nonlinear separating surface on the entire dataset which creates unwieldy storage problems. This paper proposes a novel design algorithm, called the extractive support vector algorithm, which provides improved learning speed and a vastly improved performance. Instead of learning and training with all input patterns, the proposed algorithm selects support vectors from the input patterns and uses these support vectors as the training patterns. Experimental results reveal that our proposed algorithmprovides near optimal solutions and outperforms the existing design algorithms. In addition, a significant framework which is based on extractive support vector algorithm is proposed for image restoration. In the framework, input patterns are classified by three filters: weighted order statistics filter, alpha-trimmed mean filter and identity filter. Our proposed filter can achieve three objectives: noise attenuation, chromaticity retention, and preservation of edges and details. Extensive simulation results illustrate that our proposed filter not only achieves these three objectives but also possesses robust and adaptive capabilities, and outperforms other proposed filtering techniques.

Słowa kluczowe

EN

support vector machine; unwieldy storage; image restoration; weighted order statistic filter; alpha-trimmed mean filter

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2009
• Tom: Vol. 90, nr 1-2
• Strony: 171--190
• Opis fizyczny: bibliogr. 28 poz., fot., tab., wykr.

Twórcy

autor

Yao C-C.

autor

Yu P-T.

autor

Hung R-W.

• Department of Computer Science and Information Engineering Chaoyang University of Technology, Wufong, Taichung 41349, Taiwan,

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