The goal of content-based image retrieval(CBIR) is uppermost to reduce the semantic gap between the simple visual features and the abundant semantics delivered by an image

and a critical point in the advancement of content-based retrieval is the image semantic modeling and extraction. This paper reviews the state of the art of image retrieval using semantics mainly focusing upon two aspects: image semantic modeling and image semantic extraction. In the paper

image semantic model is generalized firstly as three main components: image semantic knowledge

image semantic hierarchical model and semantic extraction hierarchical model. Then some typical methods of semantics extraction of are analyzed in detail by classifying into five classes: semantic by user interaction

user query as visual semantic template

objects and their layout recognition

scene and event semantics extraction

emotion semantics extraction

and some limitation of them are pointed out. Finally

three critical problems including object modeling and recognition

semantic knowledge base and user retrieval model faced in image semantic processing are explained

and some resolved strategies are presented elementarily.