Latent Topic Fusion-Based Cross-Media Image Semantic Annotation
Ji Liu · Dianzi xuebao · 2014
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 w eight 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.