With　the　rapid　development　of　social　media,　the　topics　emerge　and　propagate　in　a　variety　of　media　websites.　Although　much　work　has　been　done　since　NIST　proposed　the　problem　of　topic　detection　and　tracking　(TDT),　most　of　them　focus　on　single　media　data　and　are　mainly　based　on　unsupervised　clustering　method,　which　does　not　use　some　side　information　to　help　detecting　topics.　Therefore,　traditional　TDT　approaches　are　not　competent　for　cross-media　topic　detection.　To　efficiently　use　the　information　contained　in　multi-modal　data　from　different　sources　and　the　prior　knowledge,　we　propose　a　semi-supervised　co-clustering　approach　for　cross-media　topic　detection　by　a　constrained　non-negative　matrix　factorization.　The　correctness　and　convergence　of　our　approach　are　proved　to　demonstrate　its　mathematical　rigorousness.　Experiments　on　the　cross-media　dataset　verify　the　effectiveness　of　our　proposed　approach.　©　2014,　Springer-Verlag　London.