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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [15 references]  Display  Hide  Download

https://hal.inria.fr/hal-00844640
Contributor : Patrick Gros <>
Submitted on : Thursday, October 20, 2016 - 1:47:25 PM
Last modification on : Friday, November 16, 2018 - 1:24:29 AM

File

Ncibi-Mmedia2012.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License

Identifiers

  • HAL Id : hal-00844640, version 1

Citation

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⟩

Share

Metrics

Record views

1616

Files downloads

119