Visual　context　between　objects　is　an　important　cue　for　object　position　perception.　How　to　effectively　represent　the　visual　context　is　a　key　issue　to　study.　Some　past　work　Introduced　task-driven　methods　for　object　perception,　which　led　a　large　coding　quantity.　This　paper　proposes　an　approach　that　incorporates　feature-driven　mechanism　into　object-driven　context　representation　for　object　locating.　As　an　example,　the　paper　discusses　how　a　neuronal　network　encodes　the　visual　context　between　feature　salient　regions　and　human　eye　centers　with　as　little　coding　quantity　as　possible.　A　group　of　experiments　on　efficiency　of　visual　context　coding　and　object　searching　are　analyzed　and　discussed,　which　show　that　the　proposed　method　decreases　the　coding　quantity　and　improve　the　object　searching　accuracy　effectively.