参数自适应的半监督复合核支持向量机图像分类

半监督复合核支持向量机在构造聚类核时,普遍存在复杂度高、不适于大规模图像分类的问题;且K均值(K-means)图像聚类的参数难以估计。针对上述问题,提出基于均值漂移(Mean-Shift)参数自适应的半监督复合核支持向量机图像分类方法。结合Mean-Shift对像素点进行聚类分析以避免K-means图像聚类的局限性;利用图像的结构特征自适应算法参数以避免算法的波动性;由Mean-Shift结果构造Mean Map聚类核以增强同一聚类中的样本属于同一类别的可能性,使复合核更好地指导支持向量机对图像分类。实验验证了改进的聚类算法和参数取值方法可以更好地获取图像的聚类信息,使算法对普通图像和加噪图像的分类正确率较对比的半监督算法一般情况下提高1~7个百分点,且对于较大规模图像也有一定适用性,能够更高效、更稳定地进行图像分类。

Semi-supervised composite kernel support vector machine image classification with adaptive parameters

When the semi-supervised composite kernel Support Vector Machine (SVM) constructing cluster kernel, the universal existence problem is high complexity and not suitable for large-scale image classification. In addition, when using K-means algorithm for image clustering, the parameter is difficult to estimate. In allusion to the above problems, semi-supervised composite kernel SVM image classification method based on adaptive parameters of Mean-Shift was proposed. This method combined with Mean-Shift to make a cluster analysis of the pixel to avoid the limitations of K-means algorithm for image clustering, determined the parameters adaptively by using the structure feature of the image to avoid the volatility of the algorithm, and constructed Mean Map cluster kernel with Mean-Shift image clustering results to enhance the possibility of the same clustering samples belong to the same category, so as to make the composite kernel function guide SVM image classification better. The experimental results show that the improved clustering algorithm and parameter selection method can obtain the image clustering information better, the classification rate of the proposed method to ordinary and noise image can generally increase more than 1-7 percentage points compared with the other semi-supervised methods, and it has some applicability for the larger scale images, make the image classification more efficiently and stably.