Mining Frequent Patterns in 9DLT Video Databases

Multimedia database systems are becoming increasingly popular owing to the widespread use of audio-video equipment, digital cameras, CD-ROMs, and the Internet. Therefore, mining frequent patterns from video databases has attracted increasing attention in recent years. In this thesis, we proposed a novel algorithm, FVP-Miner (Frequent Video Pattern Miner), to mine frequent patterns in a video database. Our proposed algorithm consists of two phases. First, we transform every video into 9DLT strings. Second, we find all frequent image 2-patterns from the database and then recursively mine the frequent patterns in the spatial and temporal dimension. We employ three pruning strategies to prune many impossible candidates, and the concept of projected database to localize the support counting, pattern joining, and candidate pruning on the projected database. Therefore, our proposed algorithm can efficiently mine the frequent patterns in a video database. The experiment results show that our proposed method is efficient and scalable, and outperforms the modified Apriori algorithm in several orders of magnitude.