We present methods for semantic annotation of multimedia data. The goal is to detect semantic attributes (also referred to as concepts) in clips of video via analysis of a single keyframe or set of frames. The proposed methods integrate high performance discriminative single concept detectors in a random field model for collective multiple concept detection. Furthermore, we describe a generic framework for semantic media classification capable of capturing arbitrary complex dependencies between the semantic concepts. Finally, we present initial experimental results comparing the proposed approach to existing methods.