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Can I read Probabilistic Temporal Multimedia Data Mining on EtoBox?
Probabilistic Temporal Multimedia Data Mining by Chidansh Bhatt; Mohan Kankanhalli is a Computer Science article available to read on EtoBox.
What is Probabilistic Temporal Multimedia Data Mining about?
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 counti
Who reads Probabilistic Temporal Multimedia Data Mining?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Chidansh Bhatt; Mohan Kankanhalli
- Publisher
- ACM
- Published
- 2011
- Language
- EN
- Field
- Computer Science (Physical Sciences)