基于均值漂移的半监督支持向量机图像分类

标签均值半监督支持向量机（meanS3VM）在图像分类中随机选取少量无标记样本训练分类器的正确率较低，且其参数取值使结果波动性较大，针对这一问题，提出基于均值漂移（mean shift）的meanS3VM图像分类方法。以mean shift平滑图为分类对象，以降低图像特征多样性；在每个平滑区域随机选取一个样本作为无标记样本，以保证其携带对分类有用的信息而得到高效的分类器；探讨并改进参数取值方法，网格寻优敏感参数，参数ep结合支持向量机(SVM)预分类和mean shift结果估计，以获取更好更稳定的结果。实验结果表明，所提方法对普通和加噪图像的分类正确率比改进参数取值的原算法分别平均提高1和5个百分点以上，获得了更高的时间效率，且有效避免了分类结果的波动性，适用于图像分类。

Semi-supervised support vector machine for image classification based on mean shift

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.