Abstract
This paper proposes a novel framework for semantic indexing and retrieval in digital video. The components of the framework are probabilistic multimedia objects (multijects) and a network of such objects (multinets). The main contribution of this paper is a novel application of a factor graph framework to model the interactions in a network of multijects (multinet) at a semantic level. Factor graphs are statistical graphical models that provide an efficient framework for exact and approximate inference via the sum-product algorithm. Incorporating the statistical interactions between the concepts using factor graphs enhances the detection probability of individual multijects and provides a unified framework for integrating multiple modalities and supports inference of unobservable concepts based on their relation with observable concepts. Our experiments reveal significant performance improvement using the inference on the factor graph models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
