In　this　paper　we　introduce　a　new　shape-driven　approach　for　object　segmentation.　Given　a　training　set　of　shapes,　we　first　use　deep　Boltzmann　machine　to　learn　the　hierarchical　architecture　of　shape　priors.　This　learned　hierarchical　architecture　is　then　used　to　model　shape　variations　of　global　and　local　structures　in　an　energetic　form.　Finally,　it　is　applied　to　data-driven　variational　methods　to　perform　object　extraction　of　corrupted　data　based　on　shape　probabilistic　representation.　Experiments　demonstrate　that　our　model　can　be　applied　to　dataset　of　arbitrary　prior　shapes,　and　can　cope　with　image　noise　and　clutter,　as　well　as　partial　occlusions.