A Novel Semantic Statistical Model for Automatic Image Annotation Using the Relationship between the Regions Based on Multi-Criteria Decision Making

Abstract

Automatic image annotation has emerged as an important research topic due to the existence of the semantic gap and in addition to its potential application on image retrieval and management. In this paper we present an approach which combines regional contexts and visual topics to automatic image annotation. Regional contexts model the relationship between the regions, whereas visual topics provide the global distribution of topics over an image. Conventional image annotation methods neglected the relationship between the regions in an image, while these regions are exactly explanation of the image semantics, therefore considering the relationship between them are helpful to annotate the images. The proposed model extracts regional contexts and visual topics from the image, and incorporates them by MCDM (Multi Criteria Decision Making) approach based on TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method. Regional contexts and visual topics are learned by PLSA (Probability Latent Semantic Analysis) from the training data. The experiments on 5k Corel images show that integrating these two kinds of information is beneficial to image annotation. DOI: http://dx.doi.org/10.11591/ijece.v4i1.4598