Semantic feature extraction based on video abstraction and temporal modeling

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

1 Citation (Scopus)

Abstract

This paper presents a novel scheme of object-based video indexing and retrieval based on video abstraction and semantic event modeling. The proposed algorithm consists of three major steps; Video Object (VO) extraction, object-based video abstraction and statistical modeling of semantic features. Semantic feature modeling scheme is based on temporal variation of low-level features in object area between adjacent frames of video sequence. Each semantic feature is represented by a Hidden Markov Model (HMM) which characterizes the temporal nature of VO with various combinations of object features. The experimental results demonstrate the effective performance of the proposed approach.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science
EditorsJ.S. Marques, N. Perez de la Blanca, P. Pina
Pages392-400
Number of pages9
Volume3522
EditionI
Publication statusPublished - 2005
Externally publishedYes
EventSecond Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005 - Estoril, Portugal
Duration: 2005 Jun 72005 Jun 9

Other

OtherSecond Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005
CountryPortugal
CityEstoril
Period05/6/705/6/9

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ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Cite this

Lee, K. (2005). Semantic feature extraction based on video abstraction and temporal modeling. In J. S. Marques, N. Perez de la Blanca, & P. Pina (Eds.), Lecture Notes in Computer Science (I ed., Vol. 3522, pp. 392-400)