A method of scene description based on the conditional random field model is presented in this paper. The conditional random fields models the posterior directly

so that it can exploit several types of features

and has the ability to contact context information. Therefore

the CRF model in the scene description can get a more accurate description of the results. In this paper

the images are divided into rectangular blocks with a size of ×. The color feature

texture feature

and location feature for each rectangular block are extracted through multi-class features extraction. These features are clustered by the K-means algorithm

and then the feature vector is composed of the features clustered by K-means in accordance with the position of the rectangle. The feature vector is modeled by the CRF model. The model parameters are estimated through training. We use the MPM algorithm for model inference to get the scene description. The experimental results show a higher accuraly of the method presented in this paper for scene description.