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.

Semi-Supervised Support Vector Machine using label mean (meanS3VM) for image classification selects a small number of unlabeled instances randomly to train the classifier, and the classification accuracy is low; meanwhile, the parameter's determination always derives much oscillation of the results. In allusion to the above problems, meanS3VM image classification method based on mean shift was proposed. The smoothed image acquired by mean shift was used as original segmented image to reduce diversities of image features; an instance in each smoothed area was randomly selected as unlabeled instance to ensure that it carried useful information for classification and had a more efficient classifier; and the parameters value were also investigated and improved, the grid search method was used for sensitive parameters, the parameter ep was estimated by combining with Support Vector Machine (SVM) mean shift results, so that there will be a better and more stable result. The experimental results indicate that the classification rate of the proposed method to ordinary and noise image can be averagely increased more than 1% and 5%, and it has higher efficiency and avoids the oscillation of the results effectively, which is suitable for image classification.