Image semantic annotation is an important issue in image semantic analysis research.Based on the topic model

this paper proposes a novel cross-media image annotation approach for propagating the semantics among images.First

the topic model is used to capture the latent semantic topics from the visual and textual modal information in the training images.Then

a fused topic distribution is learned by merging the topic distribution of each modality using a weight parameter.Finally

an annotation model based on the fused topic distribution is trained to assign the target images using appropriate semantics.A comparison of the proposed approach with the recent state-of-the-art annotation approaches on the standard MSRC and Corel5K datasets is presented

and a detailed evaluation of the performance shows the validity of our approach.