基于视觉注意和改进隶属度的FSVM彩色图像分割

针对SVM进行图像分割时存在对噪声和孤立点较敏感导致分割结果不佳和抗造性能低下等问题,提出一种基于视觉注意和改进隶属度的FSVM (Modified fuzzy SVM,MFSVM)彩色图像分割方法.该方法在考虑人类视觉显著性检测机制因素的同时,对标准的模糊SVM算法进行改进,新的隶属度函数综合考虑了样本点距离类中心的远近以及样本点的疏密程度,从而有效惩罚噪声点并增强了支持向量的作用.通过彩色图像分割进行验证,结果显示与标准的SVM及基于样本疏密程度隶属度的FSVM分割方法相比,本文方法能够对复杂场景下的彩色进行有效分割,同时呈现出良好的抗噪能力.

Method for Image Segmentation Based on Visual Attention and FSVM with Improved Membership

Due to the interference effect of the existence of the isolated points and prone points, SVM-based segmentation algorithm cannot obtain an ideal segmentation effect.An image segmentation method based on visual attention and fuzzy SVM (MFSVM) with improved membership degree function is proposed.In order to avoid the interference from non-vital training samples and noises, the segmentation result is coincided with the characteristics of human vision.The new membership degree function not only considers the distance of samples to center, but also divides the sample points into two types according to the distance of sample points to the center.It has enhanced the effect of support vectors and can effectively improve the segmentation accuracy.Multiple sets of color images are selected to verify the effectiveness of the proposed method.Result shows that, comparing with the standard SVM and FSVM methods, the proposed method shows an effective segmentation result as well as good noise immunity ability.