Proceedings chapter
Open access

Combining Multimodal Preferences for Multimedia Information Retrieval

Presented at Augsburg (Germany), Sept. 24-29
Publication date2007

Representing and fusing multimedia information is a key issue to discover semantics in multimedia. In this paper we address more specifically the problem of multimedia content retrieval by first defining a novel preference-based representation particularly adapted to the fusion problem, and then, by investigating the RankBoost algorithm to combine those preferences and a learn multimodal retrieval model. The approach has been tested on annotated images and on the complete TRECVID 2005 corpus and compared with SVMbased fusion strategies. The results show that our approach equals SVM performance but, contrary to SVM, is parameter free and faster.

Citation (ISO format)
BRUNO, Eric, KLUDAS, Jana, MARCHAND-MAILLET, Stéphane. Combining Multimodal Preferences for Multimedia Information Retrieval. In: Proceedings of the 9th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2007. Augsburg (Germany). [s.l.] : [s.n.], 2007. p. 71–78. doi: 10.1145/1290082.1290095
Main files (1)
Proceedings chapter (Published version)

Technical informations

Creation03/06/2015 5:12:10 PM
First validation03/06/2015 5:12:10 PM
Update time03/14/2023 10:58:52 PM
Status update03/14/2023 10:58:52 PM
Last indexation08/29/2023 3:10:14 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack