Multi-label image annotation based on multi-model

Image automatic annotation is a promising and essential step for semantic image retrieval, and it's still a challenge because of the open problem of semantic gap. Recently, most of image annotation approaches paid more attention to detect single label for an image, but in fact they are multi-label learning problems. In this paper, we propose a new multi-model method for image multi-label annotation, which includes two different models for foreground and background semantic detection in terms of their distinct characters of semantic and visual features respectively, and a semantic correlation analysis model for refining the annotation results. A new visual saliency analysis algorithm based on multi-feature is proposed to obtaining the salient object, and multiple Nyström-approximating kernel discriminant analysis is used to acquire foreground semantic concept. Region semantic analysis is proposed to get annotation words of background, and semantic correlation matrix constructed by Latent Semantic Analysis is used to remove the unreliable labels. Experimental results show that our multi-model image labeling method could achieve promising performance for multi-labeling, and outperform previous methods on benchmark datasets.