Abstract 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 keywordsoriented 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 assign detailed 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.
2011, Vol. 24 
