This　letter　presents　an　approach　to　extracting　a　target　object　interactively　from　a　given　pair　of　stereo　images.　First,　a　user　marks　a　few　parts　of　the　object　and　background　in　either　of　the　two　views　with　strokes.　The　marked　pixels　are　used　to　generate　the　prior　models　of　the　foreground　and　background.　Second,　a　graph　is　constructed　with　constraints　formulated　by　the　priors　of　foreground/background,　similarities　between　intraview　neighbor　pixels　and　correspondences　between　interview　pixels.　Third,　two　segments　of　the　foreground　are　extracted　from　the　two　views　by　optimization　of　the　graph　via　graph　cut.　Traditional　methods　generally　define　the　priors　and　neighbor　similarities　in　RGB　space.　Differently,　the　proposed　method　integrates　disparity　distributions　of　foreground/background　to　enrich　the　priors　and　defines　the　similarity　metric　between　neighbor　pixels　in　RGB-D　space.　The　proposed　method　that　utilizes　RGB-D　hybrid　constraints　generates　stereo　segments　with　accuracies　higher　than　those　obtained　by　state-of-the-art　methods.