Automatic semantic content extraction in videos using a fuzzy ontology and rule-based model

Yakup Yildirim, Adnan Yazici, Turgay Yilmaz

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

Recent increase in the use of video-based applications has revealed the need for extracting the content in videos. Raw data and low-level features alone are not sufficient to fulfill the user 's needs; that is, a deeper understanding of the content at the semantic level is required. Currently, manual techniques, which are inefficient, subjective and costly in time and limit the querying capabilities, are being used to bridge the gap between low-level representative features and high-level semantic content. Here, we propose a semantic content extraction system that allows the user to query and retrieve objects, events, and concepts that are extracted automatically. We introduce an ontology-based fuzzy video semantic content model that uses spatial/temporal relations in event and concept definitions. This metaontology definition provides a wide-domain applicable rule construction standard that allows the user to construct an ontology for a given domain. In addition to domain ontologies, we use additional rule definitions (without using ontology) to lower spatial relation computation cost and to be able to define some complex situations more effectively. The proposed framework has been fully implemented and tested on three different domains. We have obtained satisfactory precision and recall rates for object, event and concept extraction.

Original languageEnglish
Article number6007134
Pages (from-to)47-61
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume25
Issue number1
DOIs
Publication statusPublished - Jan 1 2013

Keywords

  • fuzziness
  • ontology
  • Semantic content extraction
  • video content modeling

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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