ISSN : 1796-2048
Volume : 4    Issue : 5    Date : October 2009

Multimodal Preference Aggregation for Multimedia Information Retrieval
Eric Bruno and Stéphane Marchand-Maillet
Page(s): 321-329
Full Text:
PDF (437 KB)

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
through the joint design of an original multimodal information representation and of a machine
learning-based fusion algorithm. We first define a novel preference-based representation
particularly adapted to the retrieval problem, and then, we investigate the RankBoost algorithm to
combine those preferences to fullfill a user’s query. Interestingly, it ends up being a flexible retrieval
model that only manipulates ranking information and is blind to the intrinsic properties of the
multimodal information input. The approach is tested on annotated images and on the complete
TRECVID 2005 corpus and compared with SVM-based fusion strategies. The results show that our
approach equals SVM performance but, contrary to SVM, is parameter free and faster.

Index Terms