Another automated background removal method that worked best for me on complex backgrounds was Graphcut Algorithm, by Microsoft research. Gives the best results and has a implementation in OpenCV.
I think it is called GrabCut - http://research.microsoft.com/en-us/um/cambridge/projects/vi...
The disadvantage compared to the parent is that it's a semi-supervised algorithm: it requires a bounding box to be drawn around the desired image. If you had a bunch of very similar images you could pre-generate the Gaussian Mixture Model that GrabCut expects and turn it into a supervised learning algorithm, but then you'd lose the major innovation of GrabCut compared to the graph cut methods discovered before it: you would not be able to re-run with the newest "best-guess" and be guaranteed that the energy would monotonically decrease. It would also fail spectacularly if it encountered something it hadn't seen before.