Audio-visual event detection using duration dependent input output Markov models

M. R. Naphade, A. Garg, T. S. Huang

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

Abstract

Analysis of audio-visual data and detection of semantic events with spatio-temporal support is a challenging multimedia understanding problem. The difficulty lies in the gap that exists between low level media features and high level semantic concept. We introduce a duration dependent input output Markov model (DDIOMM) to detect events based on multiple modalities. The DDIOMM combines the ability to model non-exponential duration densities with the mapping of input sequences to output sequences. We test the DDIOMM by modelling the audio-visual event explosion. We compare the detection performance of the DDIOMM with the IOMM as well as the HMM. Experiments reveal that modeling of duration improves detection performance.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 2001
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages39-43
Number of pages5
ISBN (Electronic)0769513549, 9780769513546
DOIs
StatePublished - 2001
EventIEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 2001 - Kauai, United States
Duration: Dec 14 2001 → …

Publication series

NameProceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 2001

Other

OtherIEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 2001
CountryUnited States
CityKauai
Period12/14/01 → …

ASJC Scopus subject areas

  • Computer Science(all)

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