Multi-model Fused Framework for Image Annotation

Automatic image semantic annotation has become an important research topic that attracts widespread attention due to the existence of semantic gap.We propose a new framework for image multi-label annotation, in which image annotation is divided into two parts:foreground concepts detection and background labels annotation.A new multi-feature fusion based visual saliency analysis algorithm is proposed for foreground region detection in this paper, which is the basis for accurate foreground annotation.We also propose a new region semantic analysis algorithm for background labels annotation.Furthermore, a semantic correlation model based on latent semantic analysis is proposed to remove the wrong labels to achieve more accurate annotation results.Our automatic image annotation framework which fuses three different models has been evaluated on the Corel 5K and Pascal VOC 2007 databases, and compared with previous algorithms.Experimental results show that the proposed multi-model method can achieve promising performance and significantly outperforms previous algorithms.