Cross-Modal Saliency Correlation for Image Annotation

Abstract

Automatic image annotation is an attractive service for users and administrators of online photo sharing websites. In this paper, we propose an image annotation approach exploiting the crossmodal saliency correlation including visual and textual saliency. For textual saliency, a concept graph is firstly established based on the association between the labels. Then semantic communities and latent textual saliency are detected; For visual saliency, we adopt a dual-layer BoW (DL-BoW) model integrated with the local features and salient regions of the image. Experiments on MIRFlickr and IAPR TC-12 datasets demonstrate that the proposed method outperforms other state-of-the-art approaches.