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Machine Learning and Content-Based Multimedia Retrieval

Philippe-Henri Gosselin 1 David Picard 2 
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 MIDI - Multimedia Indexation and Data Integration
ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
Abstract : This paper presents an overview of popular retrieval tech- niques based on machine learning for content based multimedia retrieval. Furthermore, we also propose to highlight current gaps and required im- provement in this context. We first introduce common retrieval problems, and the usual models and assumptions made on multimedia data. Thanks to these assumptions, techniques based on machine learning can be used in many application cases. In this scope, we present popular methods for indexing multimedia data, like the ones based on the training of visual dic- tionaries. Then, we present supervised techniques that use labeled data to train and design retrieval components. We show how this last topic could benefit from many improvement from the machine learning community. Finally, this paper presents interesting perspective and new paradigms for multimedia retrieval based on machine learning.
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Submitted on : Monday, September 23, 2013 - 12:54:50 PM
Last modification on : Friday, August 5, 2022 - 2:45:59 PM
Long-term archiving on: : Tuesday, December 24, 2013 - 4:31:14 AM


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  • HAL Id : hal-00864824, version 1


Philippe-Henri Gosselin, David Picard. Machine Learning and Content-Based Multimedia Retrieval. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2013, Bruges, Belgium. pp.251-260. ⟨hal-00864824⟩



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