Semantic video annotation by mining association patterns from visual and speech features

Vincent S. Tseng, Ja Hwung Su, Jhih Hong Huang, Chih Jen Chen

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

2 Scopus citations

Abstract

In this paper, we propose a novel approach for semantic video annotation through integrating visual features and speech features. By employing statistics and association patterns, the relations between video shots and human concept can be discovered effectively to conceptualize videos. In other words, the utilization of high-level rules can effectively complement the insufficiency of statistics-based methods in dealing with broad and complex keyword identification in video annotation. Empirical evaluations on NIST TRECVID video datasets reveal that our proposed approach can enhance the annotation accuracy substantially.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings
Pages1035-1041
Number of pages7
DOIs
StatePublished - 9 Jun 2008
Event12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008 - Osaka, Japan
Duration: 20 May 200823 May 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5012 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008
CountryJapan
CityOsaka
Period20/05/0823/05/08

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  • Cite this

    Tseng, V. S., Su, J. H., Huang, J. H., & Chen, C. J. (2008). Semantic video annotation by mining association patterns from visual and speech features. In Advances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings (pp. 1035-1041). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5012 LNAI). https://doi.org/10.1007/978-3-540-68125-0_110