We present an unsupervised approach for learning a generative layered representation of a scene from a video for motion segmentation. The learnt model is a composition of layers, which consist of one or more segments. Included in the model are the effects of image projection, lighting, and motion blur. The two main contributions of our method are: (i) A novel algorithm for obtaining the initial estimate of the model using efficient loopy belief propagation; (ii) Using $\alpha\beta$-swap and $\alpha$-expansion algorithms, which guarantee a strong local minima, for refining the initial estimate. Results are presented on several classes of objects with different types of camera motion. We compare our method with the state of the art and demonstrate significant improvements.

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