This paper presents an interactive video event retrieval system based on improved adaboost learning. This system consists of three main steps. Firstly, a long video sequence is partitioned into several video clips by using a distribution-based approach instead of detecting shot transition boundaries. Secondly, audiovisual features (i.e., color, motion and audio features) are extracted from video sequences for video clip representation. Finally, the modified AdaBoost learning algorithm is employed for interactive video retrieval with relevance feedback. This AdaBoost learning algorithm differs from conventional AdaBoost learning methods mainly in the selection of paired video features for the weak classifiers. Experimental results show improved performance of video retrieval by using the proposed system.