Discriminative Model Fusion for Semantic Concept Detection and Annotation in Video

In this paper we describe a general information fusion algorithm that an be used to in orporate multimodal ues in building user-defined semantic concept models.We compare this technique with a Bayesian Network-based approach on a semantic concept detection task. Results indicate that this technique yields superior performance.We demonstrate this approach further by building classifiers of arbitrary concepts in a score space defined by a pre-deployed set of multimodal concepts.Results show annotation for user-defined concepts both in and outside the pre-deployed set is competitive with our best video-only models on the TREC Video 2002 corpus.

By: Giridharan Iyengar, Harriet J. Nock, Chalapathy Neti

Published in: RC22873 in 2003


This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.


Questions about this service can be mailed to reports@us.ibm.com .