Recently　top　performing　cross-media　topic　detection　employs　Similarity　Diffusion　Process　(SDP)　to　rank　the　interestingness　of　topics　from　a　large　number　of　candidates.　SDP　models　the　polysemous　phenomenon　from　short　and　less-constrained　user-generated　data　by　assuming　the　similarities　between　two　multi-media　data　should　be　divided　into　intersected　topics.　The　noise　in　SDP　plays　an　important　role　to　explain　the　generation　of　the　similarity.　However,　it　is　unclear　what　kind　of　noise　is　more　appropriate　for　different　modalities　in　cross　media:　SDP　under　different　noises　should　has　the　lower　false　positives　when　topics　are　successfully　detected.　In　this　paper,　we　provide　an　in　depth　analysis　of　two　types　of　noises　(Poisson　and　Gaussian)　for　this　task.　In　the　evaluation,　we　observe　that　the　combination　of　Poisson　noise　and　topic　sizes　performs　best　while　Gaussian　noise　has　a　faster　optimization　speed　than　that　of　Poisson　one.