结合均值漂移的基于图的半监督图像分类

针对基于图的半监督流形正则化图像分类算法需要大量无标记样本训练分类器,空间和时间复杂度高,甚至不能处理大规模图像,且对背景或目标复杂的图像分类错误率较高的问题,提出了结合均值漂移（mean shift）的基于图的半监督流形正则化图像分类算法。该方法对基于图的半监督流形正则化分类算法的改进主要体现在两方面,首先是通过mean shift算法对图像进行了平滑,以平滑后的图像作为分类对象;其次不是利用所有无标记样本,而是只采用少量无标记样本。实验结果表明:图像的平滑使得目标和背景区域的特征更为一致,从而利用较少的样本就可以提高分类器的正确率;同时大大降低了算法的复杂度,使得基于图的半监督分类算法用于分类大规模图像成为可能。

Graph-based semi-supervised method for image classification in combination with mean shift

There are some disadvantages of graph-based semi-supervised manifold regularization image classification algorithm, such as high space complexity and time complexity, and all of the labeled and unlabeled samples are involved in training. Therefore, it is hard to classify large-scale images. And high error rate often occursin images with complex background or target. In order to deal with these problems, a graph-based semi-supervised algorithm combining mean shift for image classification was proposed. The improvement of the method lay in two aspects: Firstly, mean shift method was used to smooth the image and the result replaced the original image as the image to be classified. Secondly, only a small number of unlabeled samples were used instead of all the unlabeled samples. The experimental results indicate that the proposed method can improve the classification accuracy and largely reduce the complexity. This algorithm makes it possible for graph-based semi-supervised classification algorithms to classify large-scale images.