Object　localization　becomes　considerable　research　with　the　rapid　expansion　of　computer　vision　applications.　How　to　locate　an　efficient　object　for　vision　recognition　tasks　is　constantly　a　pivotal　issue.　Unsupervised　object　localization　is　extremely　challenging　because　of　the　deletion　of　label　information.　In　the　last　decade,　based　on　a　large　amount　of　label　information,　a　lot　of　localization　algorithms　have　been　put　forward,　some　of　which　are　capable　of　obtaining　desirable　performance.　Whereas,　it　is　unaffordable　for　providing　sufficient　labelled　data　in　plenty　of　real-world　applications,　which　is　the　reason　why　unsupervised　object　localization　captured　growing　attention.　In　such　cases,　model　transfer　would　dramatically　improve　the　performance　by　evading　much　expensive　labelling　efforts.　Towards　this　end,　this　paper　focuses　on　model　transferring　for　solving　the　unsupervised　object　localization　problem.　We　propose　an　effective　method　for　utilizing　the　pre-trained　deep　convolutional　models　to　evaluate　the　correlation　of　features　and　predict　results　between　original　image　and　sub-images　which　generated　via　object　proposal.　The　most　similar　sub-image　can　precisely　locate　the　object　for　the　unlabeled　images.　There　is　one　more　point,　extensive　experiments　validate　the　effectiveness　of　the　proposed　method.　On　publicly　available　object　localization　datasets,　this　method　invariably　outperforms　existing　state-of-the-art　methods.　©　2019　IEEE.