Therefore, after analyzing the importance of frequency domain phase information and tex-tural information in characterizing image features, a novel SVM image segmentation method is proposed using phase consistency and textural features. The new method combines phase consistency statistic characteristics, textural features and gray-level characteristics into a training eigenvector and segments image with SVM classification technique. Compared with the traditional method, the statistical eigenvectors extracted by the new method can reflect details of the edges of image and textural information effectively. The experimental results show that the new method is more effective than the traditional method for SVM image segmentation, especially in the situation where there is low edge contrast and rich textural information in the image's target area.%在分析了频域相位信息和纹理信息在表征图像特征方面的重要性之后,提出了一种结合相位一致和纹理特征的SVM图像分割方法.该方法将相位一致性统计特征、纹理特征和灰度特征一起组合成训练特征向量,采用支持向量机分类方法对图像进行分割.相对于传统方法,该方法提取的统计特征向量可以有效地反映图像边缘细节和纹理信息.实验结果表明,该方法比传统的SVM图像分割方法更有效,尤其适用于图像中目标区域的边缘对比度低和纹理信息丰富的情形.
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