A LDA-based method for automatic tagging of Youtube videos

Mohamed Morchid (1) , Georges Linares (1)

1 LIA - Laboratoire Informatique d'Avignon

This article presents a method for automatic tagging of Youtube videos. The proposed method combines an automatic speech recognition (ASR) system, that extracts the spoken contents, and a keyword extraction component that aims at finding a small set of tags representing a video. In order to improve the robustness of the tagging system to the recognition errors, a video transcription is represented in a topic space obtained by a Latent Dirichlet Allocation (LDA), in which each dimension is automatically characterized by a list of weighted terms. Tags are extracted by combining the weighted word list of the best LDA classes. We evaluate this method by employing the user-provided tags of Youtube videos as reference and we investigate the impact of the topic model granularity. The obtained results demonstrate the interest of such model to improve the robustness of the tagging system.

Mots clés: Index Terms— audio categorization; structuring multimedia collection; speech recognition; keyword extraction; Index Terms— audio categorization

Domaines: Informatique [cs]

Mohamed Morchid, Georges Linares. A LDA-based method for automatic tagging of Youtube videos. 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS) , Jul 2013, Paris, France. ⟨10.1109/WIAMIS.2013.6616126⟩. ⟨hal-01319775⟩