This thesis presents a conditional probabilistic retrieval model as a principled framework to combine diverse knowledge sources. This model can integrate multiple forms of ranking features (query dependent and query independent features) as well as query information and context information in a unified framework with a solid probabilistic foundation. Under this retrieval framework, we overview and develop a number of state-of-the-art approaches for extracting ranking features from multimedia knowledge sources. In order to deal with heterogenous features, a discriminative learning approach is suggested for estimating the combination parameters. Moreover, an efficient rank learning approach has been developed to explicitly model the ranking relations in the learning process with much less training time. 
