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Revealing the Characteristics of Balinese Dance Maestros by Analyzing Silhouette Sequence Patterns Using Bag of Visual Movement with HoG and SIFT Features

Journal of ICT Research and Applications (JICTRA) · Vol. 15 No. 1 (2021) · pp. 89–104

DOI
10.5614/itbj.ict.res.appl.2021.15.1.6
ISSN
2338-5499, 2337-5787
Published
2021-07-06
Publisher
DRI - ITB
Language
eng

Abstract

The aim of this research was to reveal and explore the characteristics of Balinese dance maestros by analyzing silhouette sequence patterns of Balinese dance movements. A method and complete scheme for the extraction and construction of silhouette features of Balinese dance movements are proposed to enable performing quantitative analysis of Balinese dance movement patterns. Two different feature extraction methods, namely the Histogram of Gradient (HoG) feature and the Scale Invariant Features Transform (SIFT) descriptor, were used to build the final feature, called the Bag of Visual Movement (BoVM) feature. This research also makes a technical contribution with the proposal of quantifying measures to analyze the movement patterns of Balinese dances and to create the profile and characteristics of dance maestros/creators. Eight Balinese dances from three different Balinese dance maestros were analyzed in this work. Based on the experimental results, the proposed method was able to visually detect and extract patterns from silhouette sequences of Balinese dance movements. Quantitatively, the pattern measures for profiling of Balinese dances and maestros revealed a number of significant characteristics of different dances and different maestros.

Keywords

Collaboration network of the authors

10 authors · 18 co-authorship links

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  • Universitas Pendidikan Ganesha
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Ni Made Rai MaryatiUniversitas Pendidikan Ganesha2
I Wayan GunayaAffiliation unknown2
Gede Aditra PradnyanaUniversitas Pendidikan Ganesha1
Komang SetemenUniversitas Pendidikan Ganesha1
Ayu Nyoman Waisantini WinayaUniversitas Pendidikan Ganesha1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Revealing the Characteristics of Balinese Dance Maestros by Analyzing Silhouette Sequence Patterns Using Bag of Visual Movement with HoG and SIFT Features</dc:title>
	<dc:creator>Kesiman, Made Windu Antara</dc:creator>
	<dc:creator>Maysanjaya, I Made Dendi</dc:creator>
	<dc:creator>Pradnyana, I Made Ardwi</dc:creator>
	<dc:creator>Sunarya, I Made Gede</dc:creator>
	<dc:creator>Suputra, Putu Hendra</dc:creator>
	<dc:subject xml:lang="en-US">Balinese dance</dc:subject>
	<dc:subject xml:lang="en-US">feature</dc:subject>
	<dc:subject xml:lang="en-US">movement</dc:subject>
	<dc:subject xml:lang="en-US">pattern</dc:subject>
	<dc:subject xml:lang="en-US">sequence</dc:subject>
	<dc:description xml:lang="en-US">The aim of this research was to reveal and explore the characteristics of Balinese dance maestros by analyzing silhouette sequence patterns of Balinese dance movements. A method and complete scheme for the extraction and construction of silhouette features of Balinese dance movements are proposed to enable performing quantitative analysis of Balinese dance movement patterns. Two different feature extraction methods, namely the Histogram of Gradient (HoG) feature and the Scale Invariant Features Transform (SIFT) descriptor, were used to build the final feature, called the Bag of Visual Movement (BoVM) feature. This research also makes a technical contribution with the proposal of quantifying measures to analyze the movement patterns of Balinese dances and to create the profile and characteristics of dance maestros/creators. Eight Balinese dances from three different Balinese dance maestros were analyzed in this work. Based on the experimental results, the proposed method was able to visually detect and extract patterns from silhouette sequences of Balinese dance movements. Quantitatively, the pattern measures for profiling of Balinese dances and maestros revealed a number of significant characteristics of different dances and different maestros.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2021-07-06</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/15194</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2021.15.1.6</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 15 No. 1 (2021); 89-104</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2021.15.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/15194/5288</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Journal of ICT Research and Applications</dc:rights>
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