Sequential pattern based temporal contour representations for content-based multimedia timeline analysis

Gang Ren, Joseph Johnson, Hyunhwan Lee, Mitsunori Ogihara

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

Abstract

Temporal contour shapes are closely linked to the narrative structure of multimedia content and provide important reference points in content-based multimedia timeline analysis. In this paper, multimedia timeline is extracted from content as time varying video and audio signal features. A temporal contour representation is implemented based on sequential pattern discovery algorithm for modeling the variation contours of multimedia features. The proposed contour representation extracts repetitive temporal patterns from a hierarchy of time resolutions or from synchronized video/audio feature dimensions. The statistically significant contour components, depicting the dominant timeline shapes, are utilized as a structural or analytical representation of the timeline. The modeling performance of this proposed temporal modeling framework is demonstrated through empirical validation and subjective evaluations.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages657-664
Number of pages8
ISBN (Electronic)9781509061662
DOIs
StatePublished - Jan 31 2017
Event15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States
Duration: Dec 18 2016Dec 20 2016

Other

Other15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
CountryUnited States
CityAnaheim
Period12/18/1612/20/16

Keywords

  • Contour representations
  • Multimedia signal processing
  • Multimedia structure analysis
  • Sequential pattern

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Ren, G., Johnson, J., Lee, H., & Ogihara, M. (2017). Sequential pattern based temporal contour representations for content-based multimedia timeline analysis. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 (pp. 657-664). [7838220] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2016.159

Sequential pattern based temporal contour representations for content-based multimedia timeline analysis. / Ren, Gang; Johnson, Joseph; Lee, Hyunhwan; Ogihara, Mitsunori.

Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 657-664 7838220.

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

Ren, G, Johnson, J, Lee, H & Ogihara, M 2017, Sequential pattern based temporal contour representations for content-based multimedia timeline analysis. in Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016., 7838220, Institute of Electrical and Electronics Engineers Inc., pp. 657-664, 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Anaheim, United States, 12/18/16. https://doi.org/10.1109/ICMLA.2016.159
Ren G, Johnson J, Lee H, Ogihara M. Sequential pattern based temporal contour representations for content-based multimedia timeline analysis. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 657-664. 7838220 https://doi.org/10.1109/ICMLA.2016.159
Ren, Gang ; Johnson, Joseph ; Lee, Hyunhwan ; Ogihara, Mitsunori. / Sequential pattern based temporal contour representations for content-based multimedia timeline analysis. Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 657-664
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