1 Effective Semantic Annotation by Image-to-Concept Distribution Model

Abstract—Image annotation based on visual features has been a difficult problem due to the diverse associations exist between visual features and human concepts. In this paper, we propose a novel approach called AICDM (Annotation by Image-to-Concept Distribution Model) for image annotation by discovering the associations between visual features and human concepts from image-to-concept distribution. Through the proposed image-to-concept distribution model, visual features and concepts can be bridged to achieve high-quality image annotation. In this paper, we propose to use 'visual features', 'models', and 'visual genes ' which represent analogous functions to the biological chromosome, DNA, and gene. Based on the proposed models using entropy, tf-idf, rules, and SVM, the goal of high-quality image annotation can be achieved effectively. Our empirical evaluation results reveal that AICDM method can effectively alleviate problem of visual-to-concept diversity and achieve better annotation results than many existing state-of-the-art approaches in terms of precision and recall. Index Terms—Image annotation, tf-idf, entropy, image-to-concept distributio