Feature　representation　has　been　an　elusive　concept　until　neural　networks　become　popular　and　exhibit　the　strong　learning　capability.　However,　as　supervised　labels　provide　a　little　meaningful　information　for　feature　representation,　neural　networks　suffer　from　inadequate　ability　to　acquire　their　representing　knowledge　by　extracting　patterns　from　raw　data.　To　address　this　issue,　in　this　paper,　we　propose　a　novel　feature　learning　model　by　transferring　the　spatial　relation　information　to　the　weights　of　neural　networks.　More　specially,　privileged　knowledge　stemmed　from　segmented　image　is　fully　utilized　in　distilling　stage　to　perceive　notable　objects.　The　segmented　image　is　regarded　as　a　probability　distribution　of　objects,　the　notable　objects　are　fetched　by　maximizing　lower　bound　of　mutual　information,　instead　of　considering　each　image　as　an　example　of　one-hot　label.　Through　this　way,　spatial　relation　information　is　contributed　to　the　feature　learning　besides　class　labels.　This　strategy,　denoted　as　PKT-network,　is　applied　to　the　image　network　training.　Experimental　results　show　that　PKT-network　performs　excellently　for　multi-objects　representation　on　Pascal　VOC　2012　SegmentationClass　(20　object　classes)　dataset　and　Microsoft　COCO　(80　object　classes)　dataset.　(C)　2017　Elsevier　B.V.　All　rights　reserved.