Effective image semantic annotation by discovering visual-concept associations from image-concept distribution model

Up to the present, the contemporary studies are not really successful in image annotation due to some critical problems like diverse regularities between visual features and human concepts. Such diverse regularities make it hard to annotate the image semantics correctly. In this paper, we propose a novel approach called AICDM (Annotation by Image-Concept Distribution Model) for image annotation by discovering the associations between visual features and human concepts from image-concept distribution. Through the proposed image-concept distribution model, the uncertain regularities between visual features and human concepts can be clarified for achieving high-quality image annotation. The empirical evaluation results also reveal that our proposed AICDM method can effectively alleviate the uncertain regularity problem and bring out better annotation results than other existing approaches in terms of precision and recall.