Using probabilistic ontologies for video exploration

Miguel A. Bustamante, Jason J. Corso

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Video data is being collected at alarming rates and yet there exists no comprehensive forensic toolset that enables the analyst to quickly examine video in the context of the massive collections. This research builds a System that studies video at a semantic level by means of a joint solution to semantic entity extraction, entity-entity relationship extraction, and dynamic event recognition. The working of the System is grounded in formal ontology. This ontology is jointly induced from the data and established by the human domain experts (i.e., interactive machine learning). Specifically, we implement a Multi Entity Bayesian Network (a form of a probabilistic ontology); we test our System on two-on-two basketball game videos, and our results demonstrate state of the art detection rates on activities like passing the ball, and shooting, consequently promising that the presented methodology is an encouraging direction for semantically rich video analysis.

Original languageEnglish
Title of host publication18th Americas Conference on Information Systems 2012, AMCIS 2012
Pages1335-1348
Number of pages14
StatePublished - 2012
Event18th Americas Conference on Information Systems 2012, AMCIS 2012 - Seattle, WA, United States
Duration: 9 Aug 201212 Aug 2012

Publication series

Name18th Americas Conference on Information Systems 2012, AMCIS 2012
Volume2

Conference

Conference18th Americas Conference on Information Systems 2012, AMCIS 2012
Country/TerritoryUnited States
CitySeattle, WA
Period9/08/1212/08/12

Keywords

  • Multi entity bayesian network
  • Ontology
  • Probabilistic ontology
  • Video semantic analyses

Fingerprint

Dive into the research topics of 'Using probabilistic ontologies for video exploration'. Together they form a unique fingerprint.

Cite this