Automatic　image　annotation　is　a　critical　and　challenging　problem　in　pattern　recognition　and　image　understanding　areas.　There　are　some　problems　in　existing　automatic　image　annotation　areas.　For　example,　the　size　of　unlabeled　data　is　much　larger　than　the　labeled　data.　Besides,　most　image　annotation　models　can　only　use　one　kind　of　image　segmentation　strategy　and　certain　image　description　method.　According　to　the　above　problems,　an　automatic　image　annotation　model　based　on　Co-training　is　proposed.　In　this　model,　four　independent　feature　properties　are　constructed　and　then　four　corresponding　sub-classifers　are　built.　In　this　way,　different　image　segmentation　strategies　and　feature　representation　methods　can　be　integrated　into　a　unified　framework.　An　adaptive　algorithm　based　on　vote　and　consistency　is　proposed　to　extend　the　training　dataset.　The　proposed　method　use　Co-training　algorithm　and　mass　unlabeled　data　to　improve　the　performance　of　automatic　image　annotation.　Experiments　conducted　on　Corel　5　K　dataset　verify　the　effectiveness　of　proposed　method.