Semantic Vocabulary Cognition Model for the Improvement of Automatic Image Annotation

Automatic annotating images by equipment is of great interest as it meets one's common need for retrieving image content. Usually image content description with keywords is regarded as a visual-word correlation process. However, in view of the viewer's psychology, image to words is a kind of cognition process, which depends more on the experience for one to understand what's in an image. In this paper, we introduce a semantic vocabulary cognition model to improve the image annotation result. In the training process, images are annotated using common probability model that computes the correlation between images and the keywords. Then a semantic vocabulary topic is computed and compared with the words correlation described in WordNet. Finally the divergence of the two distribution is computed to remove the irrational annotations. Experimental results show that the annotation results are improved through this model.