It is challenging to segment moving objects in a video sequence viewed from a freely moving camera. Within the conditional random field framework, we propose a new segmentation cue to solve this problem. The cue is defined as a spatial-color likelihood ratio map by combining the foreground and background appearance models which are extracted from the previous segmentation result. Specifically, to make the cue suitable for the current segmentation task in the presence of complex camera motions, we introduce a camera motion compensation step in the process of evaluating the likelihood ratio map. We validate the proposed segmentation cue using several video sequences taken by hand-held cameras in outdoor urban scenes.