Typically,　k-means　clustering　or　sparse　coding　is　used　for　codebook　generation　in　the　bag-of-visual　words　(BOW)　model.　Local　features　are　then　encoded　by　calculating　their　similarities　with　visual　words.　However,　some　useful　information　is　lost　during　this　process.　To　make　use　of　this　information,　in　this　paper,　we　propose　a　novel　image　representation　method　by　going　one　step　beyond　visual　word　ambiguity　and　consider　the　governing　regions　of　visual　words.　For　each　visual　application,　the　weights　of　local　features　are　determined　by　the　corresponding　visual　application　classifiers.　Each　weighted　local　feature　is　then　encoded　not　only　by　considering　its　similarities　with　visual　words,　but　also　by　visual　words＇　governing　regions.　Besides,　locality　constraint　is　also　imposed　for　efficient　encoding.　A　weighted　feature　sign　search　algorithm　is　proposed　to　solve　the　problem.　We　conduct　image　classification　experiments　on　several　public　datasets　to　demonstrate　the　effectiveness　of　the　proposed　method.　(C)　2014　Elsevier　Inc.　All　rights　reserved.