Image automatic annotation is a significant and challenging problem in pattern recognition and computer vision. Aiming at the problems that the existing models have low utilization and they are affected by unbalanced positive and negative samples, a hierarchical image annotation model is proposed. In the first layer, discriminative model is used to assign topic annotations to unlabeled images, and then the corresponding relevant image sets are obtained. In the second layer, a keywords-oriented method is proposed to establish links between images and keywords, and then the proposed iterative algorithm is used to expand semantic words and relevant image sets. Finally, a generative model is used to assignImage Automatic Annotation, Hierarchical Model, Relevant Image Expansion, Semantics Expansiondetailed annotations to unlabeled images on expanded relevant image sets. Hierarchical model uses less relevant training images to obtain better annotation results. Experimental results on Corel 5K datasets verify the effectiveness of proposed hierarchical image annotation model.%图像自动标注是模式识别与计算机视觉等领域中重要而又具有挑战性的问题.针对现有模型存在数据利用率低与易受正负样本不平衡影响等问题,提出了基于判别模型与生成模型的新型层叠图像自动标注模型.该模型第一层利用判别模型对未标注图像进行主题标注,获得相应的相关图像集；第二层利用提出的面向关键词的方法建立图像与关键词之间的联系,并使用提出的迭代算法分别对语义关键词与相关图像进行扩展；最后利用生成模型与扩展的相关图像集对未标注图像进行详细标注.该模型综合了判别模型与生成模型的优点,通过利用较少的相关训练图像来获得更好的标注结果.在Corel 5K图像库上进行的实验验证了该模型的有效性.
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