Mining Frequent Patterns in Image and Video Databases
Date Issued
2009
Date
2009
Author(s)
Hong, Ruey-Wen
Abstract
Because of fast growth in the volume of image and video data, how to get useful information from image and video databases has attracted more and more attention in recent years. In this dissertation, we propose three algorithms, 9DLT-Miner, 2DZ-Miner, and 3DZ-Closed algorithms. The 9DLT-Miner algorithm is to find the frequent spatial patterns in 9DLT image databases. The 2DZ-Miner algorithm is to find the frequent spatial patterns in 2DZ image databases. The 3DZ-Closed algorithm is to find the frequent closed spatial-temporal patterns in 3DZ video databases.In the 9DLT-Miner and 2DZ-Miner algorithms, in addition to using the anti-monotone pruning strategy to prune impossible candidate patterns, we utilize the characteristics of the 9DLT and 2DZ-string representations to design the relation inference matrices respectively. By using the inference matrices, we prune most impossible candidate patterns. he 3DZ-Closed algorithm uses the pattern index and pattern index tree to mine all frequent closed spatial-temporal patterns in 3DZ video databases. In the 3DZ-Closed algorithm, we not only use the 2DZ relation inference matrix to prune impossible candidate patterns, we also propose a “one-level-ahead checking” pruning strategy, which can mark the non-expandable nodes in the pattern index tree. Therefore, the 3DZ-Closed algorithm can effectively prune the unnecessary branch nodes in the pattern index tree and avoid the costly candidate generation. The experimental results show that the 9DLT-Miner, 2DZ-Miner and 3DZ-Closed algorithms outperform the Apriori-like algorithms.
Subjects
Frequent pattern mining
spatial pattern
spatial-temporal pattern
image mining
video mining
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