Aiming　at　the　problem　that　the　traditional　image　annotation　model　has　long　training　time,　sensitive　to　low-frequency　words　and　other　issues,　this　paper　proposes　a　new　automatic　image　annotation　method　based　on　Monte-Carlo　dataset　balance　and　robustness　incremental　extreme　learning　machine.　First　of　all,　training　images　of　the　public　image　library　are　segmented　into　different　areas　by　this　model　and　corresponding　seed　markup　words　are　selected　after　segmentation,　the　areas　are　matched　automatically　based　on　comprehensive　distance　algorithm　and　the　different　keywords　represent　different　areas.　Then,　for　the　huge　difference　of　different　annotated　words＇　sizes　in　the　public　database,　the　Monte　Carlo　data　set　equalization　algorithm　is　proposed　to　make　the　data　size　of　each　annotated　word　much　the　same.　And　a　multi-scale　feature　fusion　algorithm　is　proposed　to　extract　effective　features　from　different　annotated　images.　Finally,　the　robustness　incremental　limit　learning　is　proposed　to　improve　the　accuracy　of　the　discriminant　model　for　the　problems　of　the　consistency　of　the　hidden　layer　nodes　and　the　input　vector　weights　existing　in　the　traditional　limit　learning　machine.　The　experimental　results　show　that:　compared　with　traditional　algorithms　of　image　automatic　annotation,　the　methods　proposed　in　this　paper　can　implement　the　automatic　image　annotation　quickly,　and　it　is　robust　to　low　frequency　words,　and　it　is　higher　than　most　popular　models　of　automatic　image　annotation　in　terms　of　average　recall　rate,　average　accuracy　rate,　comprehensive　value　and　so　on.　©　2017,　Chinese　Institute　of　Electronics.　All　right　reserved.