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Robust TV Stream Labelling with Conditional Random Fields

Abir Ncibi 1 Emmanuelle Martienne 1 Vincent Claveau 1, * Guillaume Gravier 1 Patrick Gros 1 
* Corresponding author
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Multi-label video annotation is a challenging task and a necessary first step for further processing. In this paper, we investigate the task of labelling TV stream segments into programs or several types of breaks through machine learning. Our contribution is twofold: 1) we propose to use simple yet efficient descriptors for this labelling task, 2) we show that Conditional Random Fields (CRF) are especially suited for this task. In particular, through several experiments, we show that CRF out-perform other machine learning techniques, while requiring few training data thanks to its ability to handle the different types of sequential information lying in our data.
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Submitted on : Thursday, October 20, 2016 - 1:47:25 PM
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  • HAL Id : hal-00844640, version 1


Abir Ncibi, Emmanuelle Martienne, Vincent Claveau, Guillaume Gravier, Patrick Gros. Robust TV Stream Labelling with Conditional Random Fields. MMEDIA - 5th International Conference on Advances in Multimedia, IARIA, Apr 2013, Venise, Italy. pp.88-95. ⟨hal-00844640⟩



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