面向视频数据的时空伴随模式挖掘算法

Spatial-temporal co-occurrence pattern mining algorithm for video data

时空伴随模式是具有时空伴随关系的视频对象组合。为了从海量视频数据中快速发现符合查询条件的时空伴随模式,提出一种基于三重剪枝匹配策略的时空伴随模式发现算法——MPA。首先,利用已有的视频对象识别和跟踪模型对视频对象进行结构化提取;然后,对提取的连续帧中大量重复出现的视频对象进行压缩存储并构建索引;最后,设计基于前缀树的时空伴随模式发现算法,以快速发现符合查询条件的时空伴随模式。在真实数据集和合成数据集上的实验结果表明,与暴力搜索算法(BFA)相比,所提算法的效率提高了30%左右,且数据量越大,效率提高越明显。因此,所提算法能够快速发现海量视频数据中满足查询条件的时空伴随模式。

Spatial-temporal co-occurrence patterns refer to the video object combinations with spatial-temporal correlations.In order to mine the spatial-temporal co-occurrence patterns meeting the query conditions from a huge volume of video data quickly,a spatial-temporal co-occurrence pattern mining algorithm with a triple-pruning matching strategy —Multi-Pruning Algorithm(MPA) was proposed.Firstly,the video objects were extracted in a structured way by the existing video object detection and tracking models.Secondly,the repeated occurred video objects extracted from a sequence of frames were stored and compressed,and an index of the objects was created.Finally,a spatial-temporal co-occurrence pattern mining algorithm based on the prefix tree was proposed to discover the spatial-temporal co-occurrence patterns that meet query conditions.Experimental results on real and synthetic datasets show that the proposed algorithm improves the efficiency by about 30% compared with Brute Force Algorithm(BFA),and the greater the data volume,the more obvious the efficiency improvement.Therefore,the proposed algorithm can discover the spatial-temporal co-occurrence patterns satisfying the query conditions from a large volume of video data quickly.