In　order　to　narrow　the　semantic　gap,　user　interest　model　plays　an　important　role　in　personalized　image　retrieval.　A　novel　personalized　image　retrieval　approach　based　on　user　interest　model　is　proposed　in　this　study.　User　interest　model　is　developed　on　the　basis　of　short-term　and　long-term　interests.　(1)　Short-term　interests　are　represented　by　collecting　visual　and　semantic　features.　Visual　features　are　collected　by　MARS　relevance　feedback.　Semantic　features　are　constructed　by　building　a　mapping　from　image　low-level　visual　features　to　high-level　semantic　features　on　the　basis　of　SVM.　(2)　Long-term　interests　are　inferred　by　inference　engine　from　the　collected　short-term　interests.　Long-term　visual　features　are　collected　by　the　nonlinear　gradual　forgetting　interest　inference　algorithm　and　semantic　features　are　obtained　by　clustering　algorithm.　After　applying　to　image　retrieval,　experimental　results　show　that　the　average　recall/precision　is　significantly　improved　and　a　better　user　satisfaction　rate　is　achieved　as　well.　Furthermore,　it　demonstrates　our　model　can　be　efficiently　adapted　to　user　interests　and　matches　personalized　image　retrieval.