Image Annotation Based on Semantic Clustering and Relevance Feedback

Automatic image annotation is a promising key to semantic-based image retrieval by keywords. Most existing automatic image annotation approaches focused on exploring the relationship between images and annotation words and neglected the semantic information of the annotated keywords. In this paper we propose a semi-automatic image annotation framework. First we annotate the training images with our improved Markov model. Then the candidate annotation terms are clustered according to their semantic relationship. Finally we further optimize the annotation sets with relevance feedback from people´s cognitive learning habits. The experimental results show that the proposed approach provides a semi-automatic optimization of the multi-label image annotation results.