Proceedings Article10.1109/ICIP.2004.1421354
Optimized space frequency kernel for texture classification
M. Sabri,Javad Alirezaie +1 more
- 24 Oct 2004
- Vol. 3, pp 1521-1524
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TL;DR: The performance of SVM is improved by choosing an optimized space-frequency (SFR) kernel function and the proposed method is evaluated in a two-texture and multi-texture problems and shows significant improvement in error rates.
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Abstract: The performance of the support vector machine (SVM) algorithm is highly dependent on the choice of the kernel function suited to the problem at hand. In a support vector machine algorithm feature selection is implicitly performed by kernel function. On the other hand, feature selection is the most important stage in any texture classification algorithm. In this work, the performance of SVM is improved by choosing an optimized space-frequency (SFR) kernel function. The proposed method is evaluated in a two-texture and multi-texture problems. The results are compared with the original SVM and other recently published texture classification methods. The comparison shows a significant improvement in error rates. Improvement of more than 40% in compare with original SVM and about 60% in compare with logical operators (LO) and wavelet co-occurrence features (WCOF) are obtained.
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Citations
Support vector classification of land cover and benthic habitat from hyperspectral images
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TL;DR: A 1-D wavelet transform is applied to the pixel spectra, followed by feature extraction and SVM classification, which not only reduces the dimension of the input pixel feature vector but also improves the classification accuracy.
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Support vector machines for texture classification
TL;DR: Experimental results demonstrate the effectiveness of SVMs in texture classification, and it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architecture, while also providing solutions to problems inherent in these methods.