Visualising and clustering video data

Colin Fyfe; Wei Chuang Ooi; Hanseok Ko

doi:10.1007/978-3-540-77226-2_35

Visualising and clustering video data

Colin Fyfe^*, Wei Chuang Ooi, Hanseok Ko

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [1]. But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts [6]. We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels and show that the new mapping achieves better results than the standard Self-Organizing Map.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings
Publisher	Springer Verlag
Pages	335-344
Number of pages	10
ISBN (Print)	9783540772255
DOIs	https://doi.org/10.1007/978-3-540-77226-2_35
Publication status	Published - 2007
Event	8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007 - Birmingham, United Kingdom Duration: 2007 Dec 16 → 2007 Dec 19

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4881 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007
Country/Territory	United Kingdom
City	Birmingham
Period	07/12/16 → 07/12/19

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-77226-2_35

Cite this

Fyfe, C., Ooi, W. C., & Ko, H. (2007). Visualising and clustering video data. In Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings (pp. 335-344). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4881 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-77226-2_35

Visualising and clustering video data. / Fyfe, Colin; Ooi, Wei Chuang; Ko, Hanseok.
Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings. Springer Verlag, 2007. p. 335-344 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4881 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fyfe, C, Ooi, WC & Ko, H 2007, Visualising and clustering video data. in Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4881 LNCS, Springer Verlag, pp. 335-344, 8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007, Birmingham, United Kingdom, 07/12/16. https://doi.org/10.1007/978-3-540-77226-2_35

Fyfe C, Ooi WC, Ko H. Visualising and clustering video data. In Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings. Springer Verlag. 2007. p. 335-344. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-77226-2_35

@inproceedings{a530898e735740adbf0abbfd5a6037bb,

title = "Visualising and clustering video data",

abstract = "We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [1]. But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts [6]. We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels and show that the new mapping achieves better results than the standard Self-Organizing Map.",

author = "Colin Fyfe and Ooi, \{Wei Chuang\} and Hanseok Ko",

year = "2007",

doi = "10.1007/978-3-540-77226-2\_35",

language = "English",

isbn = "9783540772255",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "335--344",

booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings",

note = "8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007 ; Conference date: 16-12-2007 Through 19-12-2007",

}

TY - GEN

T1 - Visualising and clustering video data

AU - Fyfe, Colin

AU - Ooi, Wei Chuang

AU - Ko, Hanseok

PY - 2007

Y1 - 2007

N2 - We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [1]. But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts [6]. We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels and show that the new mapping achieves better results than the standard Self-Organizing Map.

AB - We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [1]. But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts [6]. We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels and show that the new mapping achieves better results than the standard Self-Organizing Map.

UR - https://www.scopus.com/pages/publications/38449115567

U2 - 10.1007/978-3-540-77226-2_35

DO - 10.1007/978-3-540-77226-2_35

M3 - Conference contribution

AN - SCOPUS:38449115567

SN - 9783540772255

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 335

EP - 344

BT - Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings

PB - Springer Verlag

T2 - 8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007

Y2 - 16 December 2007 through 19 December 2007

ER -

Visualising and clustering video data

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this