Relevance　feedback　can　be　considered　as　a　Bayesian　classification　problem.　For　retrieving　images　efficiently,　an　adaptive　relevance　feedback　approach　based　on　the　Bayesian　inference,　rich　get　richer　(RGR),　is　proposed.　If　the　feedback　images　in　current　iteration　are　consistent　with　the　previous　ones,　the　images　that　are　similar　to　the　query　target　are　assigned　to　high　probabilities.　Therefore,　the　images　that　are　similar　to　the　user＇s　ideal　target　are　emphasized　step　by　step.　The　experiments　showed　that　the　average　precision　of　RGR　improves　5-20%　on　each　interaction　compared　with　non-RGR.　When　compared　with　MARS,　the　proposed　approach　greatly　reduces　the　user＇s　efforts　for　composing　a　query　and　captures　user＇s　intention　efficiently.　(C)　2004　Elsevier　B.V.　All　rights　reserved.