Sequential pattern mining on multimedia data

Corentin Hardy 1 Laurent Amsaleg 2 Guillaume Gravier 2 Simon Malinowski 2 René Quiniou 3
1 ENS Rennes - École normale supérieure - Rennes
2 LinkMedia - Creating and exploiting explicit links between multimedia fragments
IRISA-D6 - MEDIA ET INTERACTIONS, Inria Rennes – Bretagne Atlantique
3 DREAM - Diagnosing, Recommending Actions and Modelling
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : Analyzing multimedia data is a challenging problem due to the quantity and complexity of such data. Mining for frequently recurring patterns is a task often ran to help discovering the underlying structure hidden in the data. In this article, we propose audio data symbolization and sequential pattern mining methods to extract patterns from audio streams. Experiments show that this task is hard and that the symbol-ization is a critical step for extracting relevant audio patterns.
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Communication dans un congrès
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database Workshop on Advanced Analytics and Learning on Temporal Data, 2015, Porto, Portugal
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Corentin Hardy, Laurent Amsaleg, Guillaume Gravier, Simon Malinowski, René Quiniou. Sequential pattern mining on multimedia data. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database Workshop on Advanced Analytics and Learning on Temporal Data, 2015, Porto, Portugal. <hal-01186446>

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