@inproceedings{e1f0b9991c2749428ed6824cfa727063,
title = "Probabilistic semantic video indexing",
abstract = "We propose a novel probabilistic framework for semantic video indexing. We define probabilistic multimedia objects (multijects) to map low-level media features to high-level semantic labels. A graphical network of such multijects (multinet) captures scene context by discovering intra-frame as well as inter-frame dependency relations between the concepts. The main contribution is a novel application of a factor graph framework to model this network. We model relations between semantic concepts in terms of their co-occurrence as well as the temporal dependencies between these concepts within video shots. Using the sum-product algorithm [1] for approximate or exact inference in these factor graph multinets, we attempt to correct errors made during isolated concept detection by forcing high-level constraints. This results in a significant improvement in the overall detection performance.",
author = "Naphade, \{Milind R.\} and Igor Kozintsev and Thomas Huang",
year = "2001",
language = "English (US)",
isbn = "0262122413",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
booktitle = "Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000",
note = "14th Annual Neural Information Processing Systems Conference, NIPS 2000 ; Conference date: 27-11-2000 Through 02-12-2000",
}