基于连续预测的半监督学习图像语义标注

为了在图像底层特征与高层语义之间建立关系,提高图像自动标注的精确度,结合基于图学习的方法和基于分类的标注算法,提出了基于连续预测的半监督学习图像语义标注的方法,并对该方法的复杂度进行分析。该方法利用标签数据提供的信息和标签事例与无标签事例之间的关系,根据邻接点(事例)属于同一个类的事实,构建K邻近图。用一个基于图的分类器,通过核函数有效地计算邻接信息。在建立图的基础上,把经过划分后的样本节点集通过基于连续预测的多标签半监督学习方法进行标签传递。实验表明,提出的算法在图像标注中的标注词的平均查准率、平均查全率方面有显著的提高。

Semi-supervised learning image semantic annotation based on sequential prediction

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 .