In this paper, we define an attributed random graph, which can be considered as a generalization of conventional ones, to include multiple attributes as well as numeric attribute instead of a single nominal attribute in random vertices and edges. Then we derive the probability equations for an attributed graph to be an outcome graph of the attributed random graph, and the equations for the entropy calculation of the attributed random graph. Finally, we propose the application areas to computer vision and machine learning using these concepts.