A Collaborative Bayesian Image Annotation Framework

Abstract

The integration of content and context information within an image annotation framework is studied, which refer to the low-level visual features and the co-occurrence of different real world objects in a probabilistic sense, respectively. Conventional annotation approaches fail to collect and utilize the context information. Therefore, we proposed a new framework, termed as Collaborative Bayesian Image Annotation (CBIA) framework. 1) In addition to the content information, the proposed system accumulates past annotation results and/or information actively provided by domain experts, from which the context knowledge is extracted. Hence, part of the system is collaboratively constructed by human users. 2) The above information is utilized through a Bayesian framework. Numerical results based on images collected from the Internet demonstrated better performance resulting from the introduction of context knowledge and information fusion.