Multimodal Preference Aggregation for Multimedia Information Retrieval | Zendy

Éric Bruno | Zendy; Stéphane MarchandMaillet | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Multimodal Preference Aggregation for Multimedia Information Retrieval

Author(s) -

Éric Bruno,

Stéphane MarchandMaillet

Publication year - 2009

Publication title -

journal of multimedia

Language(s) - English

Resource type - Journals

ISSN - 1796-2048

DOI - 10.4304/jmm.4.5.321-329

Subject(s) - computer science , preference , multimedia , multimedia information retrieval , information retrieval , human–computer interaction , economics , microeconomics

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research