关键词: 半监督学习, 支持向量机, 复合核, Mean-Shift算法, 图像分类

Abstract: 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.

Key words: semi-supervised learning, Support Vector Machine (SVM), composite kernel, Mean-Shift algorithm, image classification