In　this　paper,　we　introduce　a　novel　model　for　simultaneously　segment　and　recognize　object　using　shape　prior　information.　Given　a　set　of　training　shapes　including　many　different　object　classes,　the　target　shape　in　a　test　image　is　represented　approximately　as　a　sparse　convex　combination　of　the　training　shapes.　The　proposed　model　is　optimal　in　the　L2　criterion　between　the　unknown　true　shape　and　the　convex　combination　of　the　training　shapes.　Without　explicitly　imposing　sparsity　constraints,　the　convex　combination　coefficients　obtained　from　minimizing　the　ISE　are　natural　sparse.　The　proposed　model　is　able　to　automatically　select　the　reference　shapes　that　best　represent　the　object,　and　accurately　segment　the　image　taking　into　account　both　the　image　data　and　shape　prior　information.　It　is　different　from　the　existing　shape　prior　based　segmentation　models,　which　are　constructed　by　using　linear　combination　of　a　data-driven　term　and　a　shape　constraint　term.　In　addition,　an　intrinsic　registration　of　the　evolving　shape　is　introduced　into　the　model　for　transformation　invariance.　Numerical　experiments　on　synthetic　and　real　images　show　promising　results　and　the　potential　of　the　method　for　object　segmentation　and　recognition.　©　2015　IEEE.