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Multimedia Indexing and Retrieval with Features Association Rules Mining

Anicet Kouomou-Choupo 1 Laure Berti-Equille 1 Annie Morin 1
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The administration of very large collections of images, accentuates the classical problems of indexing and efficiently querying information. This paper describes a new method applied to very large still image databases that combines two data mining techniques: clustering and association rules mining in order to better organize image collections and to improve the performance of queries. The objective of our work is to exploit association rules discovered by mining, global MPEG-7 features data and to adapt the query processing. In our experiment, we use five MPEG-7 features to describe several thousands of still images. For each feature, we initially determine several clusters of images by using a K-mean algorithm. Then, we generate association rules between different clusters of features and exploit these rules to rewrite the query and to optimize the query-by-content processing.
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Contributor : Laure Berti-Equille <>
Submitted on : Thursday, August 9, 2018 - 5:53:54 PM
Last modification on : Friday, July 10, 2020 - 4:14:19 PM



Anicet Kouomou-Choupo, Laure Berti-Equille, Annie Morin. Multimedia Indexing and Retrieval with Features Association Rules Mining. ICME 2004 - IEEE Intl. Conference on Multimedia and Expo, Jun 2004, Taipei, Taiwan. pp.1299-1302, ⟨10.1109/ICME.2004.1394464⟩. ⟨hal-01856141⟩



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