Please use this identifier to cite or link to this item: https://doi.org/10.1145/1899412.1899421
Title: Probabilistic temporal multimedia data mining
Authors: Bhatt, C.
Kankanhalli, M. 
Keywords: Cross-modal correlation
Multimedia datamining
Multimodal datamining
Probabilistic interval based event mining
Sequence pattern mining
Issue Date: 2011
Source: Bhatt, C., Kankanhalli, M. (2011). Probabilistic temporal multimedia data mining. ACM Transactions on Intelligent Systems and Technology 2 (2). ScholarBank@NUS Repository. https://doi.org/10.1145/1899412.1899421
Abstract: Existing sequence pattern mining techniques assume that the obtained events from event detectors are accurate. However, in reality, event detectors label the events from different modalities with a certain probability over a time-interval. In this article, we consider for the first time Probabilistic Temporal Multimedia (PTM) Event data to discover accurate sequence patterns. PTM event data considers the start time, end time, event label and associated probability for the sequence pattern discovery. As the existing sequence pattern mining techniques cannot work on such realistic data, we have developed a novel framework for performing sequence pattern mining on probabilistic temporal multimedia event data. We perform probability fusion to resolve the redundancy among detected events from different modalities, considering their cross-modal correlation.We propose a novel sequence pattern mining algorithm called Probabilistic Interval based Event Miner (PIE-Miner) for discovering frequent sequence patterns from interval based events. PIE-Miner has a new support counting mechanism developed for PTM data. Existing sequence pattern mining algorithms have event label level support counting mechanism, whereas we have developed event cluster level support counting mechanism. We discover the complete set of all possible temporal relationships based on Allen's interval algebra. The experimental results showed that the discovered sequence patterns are more useful than the patterns discovered with state-of-the-art sequence pattern mining algorithms. © 2011.
Source Title: ACM Transactions on Intelligent Systems and Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/39518
ISSN: 21576904
DOI: 10.1145/1899412.1899421
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