Joint segmentation and temporal structure inference for partially-observed event sequences

Harvey Thornburg, Dilip Swaminathan, Todd Ingalls, Randal Leistikow

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Many events of interest in human activity-based multimedia applications exhibit a high degree of temporal structure. This structure generates expectancies regarding the occurrence and location of subsequent events. In the context of switching state-space models, we develop a general Bayesian framework for representing temporal expectancies and fusing them with raw sense-data to improve both event segmentation and temporal structure identification. Furthermore, we develop a new cognitive model for event anticipation which adapts to incoming sense-data in real time. Comparative advantages of the proposed framework are realized in controlled experiments involving partially-observed, quasi-periodic event streams.

Original languageEnglish (US)
Title of host publication2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006
PublisherIEEE Computer Society
Pages41-44
Number of pages4
ISBN (Print)0780397517, 9780780397514
DOIs
StatePublished - Jan 1 2006
Event2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006 - Victoria, BC, Canada
Duration: Oct 3 2006Oct 6 2006

Publication series

Name2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006

Other

Other2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006
CountryCanada
CityVictoria, BC
Period10/3/0610/6/06

ASJC Scopus subject areas

  • Signal Processing

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    Thornburg, H., Swaminathan, D., Ingalls, T., & Leistikow, R. (2006). Joint segmentation and temporal structure inference for partially-observed event sequences. In 2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006 (pp. 41-44). [4064515] (2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006). IEEE Computer Society. https://doi.org/10.1109/MMSP.2006.285265