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基于支持向量机方法的噪声图像分割 被引量:5

Segmentation of Images Corrupted by Noise Based on Fuzzy Weighted Support Vector Machine Approach
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摘要 图像分割是计算机视觉领域的关键技术之一。支持向量机方法被认为是好的学习分类方法之一,特别在小样本、高维情况下,具有较好的泛化性能。针对噪声图像的分割,提出了模糊权重支持向量机方法。分割实验表明,与经典支持向量机方法相比,模糊权重支持向量机方法具有更强的抗噪性。 Image segmentation is critical to computer vision. Support vector machine approach is considered a good candidate because of its good generalization performance. especially when the number of training samples is very small and the dimension of feature space is very high. The presented paper proposes the fuzzy weighted support vector machine approach for segmentation of images corrupted by noise. Experimental results show that the fuzzy weighted support vector machine approach is more robust than classical support vector machine approach.
出处 《微电子学与计算机》 CSCD 北大核心 2007年第11期14-16,20,共4页 Microelectronics & Computer
关键词 支持向量机 噪声图像分割 计算机视觉 统计学习理论 support vector machine noise image segmentation computer vision statistical learning theory
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参考文献6

  • 1Yan C,Sang N,Zhang T.Local entropy-based transition region extraction and thresholding[J].Patter Recognition Letters,2003,24:2935-2941
  • 2Vapnik V.The nature of statistics learning theory[M].Springer Verlag,New York,1995
  • 3Guo G,Li S Z,Chan K L.Support vector machines for face recognition[J].Image and Vision Computing,2001,19:631-638
  • 4Li S,Kwok J T,Zhu H.Texture classification using the support vector machines[J].Pattern Recognition,2003,36:2883-2893
  • 5徐海祥,朱光喜,张翔,田金文,彭复员.基于改进的一对一支持向量机方法的多目标图像分割[J].微电子学与计算机,2005,22(12):51-54. 被引量:4
  • 6Lin C F,Wang S D.Fuzzy support vector machines[J].IEEE Transactions on Neural Networks,2002,13(2):464-471

二级参考文献14

  • 1C Yan, N Sang, T Zhang. Local Entropy-based Transition Region Extraction and Thresholding. Patter Recognition Letters, 2003, 24: 2935~2941.
  • 2T Zhang, J Peng, Z Li. An Adaptive Image Segmentation Method with Visual Nonlinearity Characteristics. IEEE Transactions on Systems, Man and Cybernetics-part B:Cybernetics, 1996, 26(4): 619~627.
  • 3X Xue, et al. A New Method of SAR Image Segmentation Based on Neural Network. Fifth International Conference on Computational Intelligence and Multimedia Applications, 2003: 149~153.
  • 4T Ziemke. Radar Image Segmentation Using Recurrent Artificial Neural Networks. Pattern Recognition Letters,1996, 17: 319~334.
  • 5G Kuntimad, H S Ranganath. Perfect Image Segmentation Using Pulse Coupled Neural Networks. IEEE Transactions on Neural Networks, 1999, 10(3): 591~598.
  • 6V Vapnik. The Nature of Statistics Learning Theory.Springer Verlag, New York, 1995.
  • 7V Vapnik. Statistical Learning Theory. J Wiley, New York,1998.
  • 8G Guo, S Z Li, K L Chan. Support Vector Machines for Face Recognition. Image and Vision Computing, 2001, 19:631~638.
  • 9S Li, J T Kwok, et al. Texture Classification Using the Support Vector Machines. Pattern Recognition, 2003, 36:2883~2893.
  • 10Q Zhao, J C Principe. Support Vector Machines for SAR Automatic Target Recognition. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(2): 643~654.

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