In　this　paper,　based　on　probability　distribution　of　weak　classifier　output,　an　improved　Adaboost-based　multi-classifiers　fusion　algorithm　is　proposed　for　semantic　concept　extraction.　We　present　a　novel　method　to　compute　the　error　rate　and　the　weight　of　each　classifier.　We　believe　that　the　error　rate　of　an　example　should　be　related　to　its　rank　in　a　weak　classifier　output.　First,　the　probability　distribution　of　the　SVM　output　is　estimated.　SVM　is　regarded　as　the　weak　classifier　in　our　system.　Then,　based　on　the　negative　and　positive　examples　probability　distributions,　we　can　calculate　the　error　rates　of　positive　and　negative　example　respectively.　We　define　the　error　rate　of　a　positive　example　as　the　proportion　of　negative　examples　whose　scores　are　bigger　than　this　positive　example　in　an　SVM　output.　Finally,　we　integrate　the　error　rate　into　the　Adaboost　algorithm　and　add　some　modification　to　further　improve　our　performance.　We　call　the　proposed　fusion　method　D-Adaboost　since　the　distribution-based　error　rate　computing　algorithm　is　integrated.　Experimental　results　on　TRECVID-2007　dataset　show　the　effectiveness　of　the　proposed　D-Adaboost.　Copyright　2010　ACM.