This　paper　summarizes　our　approaches　to　the　semantic　indexing　task　in　TRECVID　2010.　Several　fusion　strategies　are　employed　in　our　detection　system,　including　a　low-level　feature　concatenation,　text　retrieval　fusion,　and　ontology　relation　fusion.　It　can　be　drawn　that　the　fusion　of　text　retrieval　results　with　a　baseline　in　a　proper　way　can　contribute　greatly　to　the　system　performance.　Our　approach　to　ontology　relation　fusion　increases　the　number　of　the　inferred　true　shots　returned.　Two　of　our　runs　using　fusion　schemes　have　the　best　infAP　for　a　feature　among　the　30　features　evaluated.　We　describe　our　text　and　ontology　relation　fusion　schemes　in　detail　in　this　paper.　The　description　of　our　submission　runs　for　the　semantic　indexing　is　as　follows.