Skip to Main content Skip to Navigation

Contributions à la détection de concepts et d'événements dans les documents vidéos

Abstract : A consequence of the rise of digital technology is that the quantity of available collections of multimedia documents is permanently and strongly increasing. The indexing of these documents became both very costly and impossible to do manually. In order to be able to analyze, classify and search multimedia documents, indexing systems have been defined. However, most of these systems suffer quality or practicability issues. Their performance is limited and depends on the data volume and data variability. Indexing systems analyze multimedia documents, looking for static concepts (bicycle, chair,...), or events (wedding, protest,...). Therefore, the variability in shapes, positions, lighting or orientation of objects hinders the process. Another aspect is that systems must be scalable. They should be able to handle big data while using reasonable amount of computing time and memory.The aim of this thesis is to improve the general performance of content-based multimedia indexing systems. Four main contributions are brought in this thesis for improving different stages of the indexing process. The first one is an "early-early fusion method" that merges different information sources in order to extract their deep correlations. This method is used for violent scenes detection in movies. The second contribution is a weakly supervised method for basic concept (objects) localization in images. This can be used afterwards as a new descriptor to help detecting complex concepts (events). The third contribution tackles the noise reduction problem on ambiguously annotated data. Two methods are proposed: a shot annotation generator, and a shot weighing method. The last contribution is a generic descriptor optimization method, based on PCA and non-linear transforms.These four contributions are tested and evaluated using reference data collections, including TRECVid and MediaEval. These contributions helped our submissions achieving very good rankings in those evaluation campaigns.
Document type :
Complete list of metadata

Cited literature [186 references]  Display  Hide  Download
Contributor : ABES STAR :  Contact
Submitted on : Friday, June 30, 2017 - 3:10:14 PM
Last modification on : Wednesday, July 6, 2022 - 4:12:37 AM
Long-term archiving on: : Monday, January 22, 2018 - 9:43:52 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01138596, version 2



Nadia Derbas. Contributions à la détection de concepts et d'événements dans les documents vidéos. Modélisation et simulation. Université de Grenoble, 2014. Français. ⟨NNT : 2014GRENM035⟩. ⟨tel-01138596v2⟩



Record views


Files downloads