Boosting　has　been　extensively　used　in　image　processing.　Many　work　focuses　on　the　design　or　the　usage　of　boosting,　but　training　boosting　on　large-scale　datasets　tends　to　be　ignored.　To　handle　the　large-scale　problem,　we　present　stochastic　boosting　(StocBoost)　that　relies　on　stochastic　gradient　descent　(SGD)　which　uses　one　sample　at　each　iteration.　To　understand　the　efficacy　of　StocBoost,　the　convergence　of　training　algorithm　is　theoretically　analyzed.　Experimental　results　show　that　StocBoost　is　faster　than　the　batch　ones,　and　is　also　comparable　with　the　state-of-the-arts.