中文摘要：

提出一种基于局部时空兴趣点的电影中复杂事件检索与识别的方法。先将一个独立的事件视频序列表示成一个自组织映射像密度图，再将此密度图与支持向量机相结合用于识别事件。该方法使用局部时空特征描述子来捕捉视频中的局部事件，可以适应事件的模式的不同的大小和速度。为了验证该方法的有效性，使用公开的Hollywood视频数据库，其中的镜头序列收集自32部不同的Hollywood电影，包含了8个事件类别。综合实验，得到平均正确率、平均查准率和平均查全率分别为0．601、0．530和0．566。实验结果表明本文方法明显提高了平均正确率和平均查准率。

英文摘要：

We propose a new method based on local space-time interest points and self-organization feature maps （SOFM） to recognize and retrieval complex events in real movie. In this method, an individual video sequence is represented as a SOFM density map, We integrate this density map with a support vector machine （ SVM ） to recognize events. Local space-time features are introduced to capture the local events in video and can be adapted to size and velocity of the pattern of the event. To evaluate the effectiveness of this method, we use the public Hollywood dataset. In this dataset shot sequences are collected from 32 different Hollywood movies and it includes eight event classes. According to the experiment, the average accuracy rate, the average precision rate, and average recall rate were 0. 601, 0. 530 and O. 566 respectively. The presented results justify the proposed method explicitly improving the average accuracy and average precision compared with other relative approaches.