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

Abstract

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.