[Purpose/significance] Semantic annotation of digital images is an effective way to solve this problem. The foundation of semantic image annotation is the construction of semantic models. This paper intends to review the existing mainstream semantic models for image annotation, and explore their advantages and disadvantages.[Method/process] Firstly, four representative semantic models for image annotation were reviewed, including Eakins model, Jaimes & Chang model, Kong model and Panofsky model, using literature survey, and then the first three models from three aspects (i.e. semantic level, extensibility and application range) were compared and analyzed using comparative analysis.[Result/conclusion] Through the above analysis, it can be concluded that Eakins model has the most comprehensive semantic level, the strongest semantic expression ability and the widest application range, whereas Kong model is the most scalable and adaptable one.
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