Abstract:

In order to establish the relationship between lowlevel features and highlevel semantics of the image,improve the accuracy of image automatic annotation,combining with graph learning and classification annotation algorithm,we propose an image semantic annotation method for sequential predictionbased semisupervised learning,and analyze the complexity of the method.According to the fact that the adjacent vertexes (cases) should belong to the same class, by using the information provided by tag datum and the relationship between tag cases and cases with no labels,the method constructs a K relative neighborhood graph.We use a graphbased classifier and a kernel function to calculate the adjacency information effectively.On the basis of building graphs,we propagate the labels of the node sets derived from the samples by sequential predictionbased semisupervised multiple labels learning method.Experiments show that the proposed algorithm for image annotation significantly improves the average precision ratio and the average recall ratio of the marked words .

Key words: sequential prediction;semi-supervised;image annotation;graph learning;multiple labels