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A Novel Texture Classification Procedure by using Association Rules

Journal of ICT Research and Applications (JICTRA) · Vol. 2 No. 2 (2013) · pp. 103–114

DOI
10.5614/itbj.ict.2008.2.2.2
ISSN
2338-5499, 2337-5787
Published
2013-09-13
Publisher
DRI - ITB
Language
eng

Abstract

Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. Association rules have been used in various applications during the past decades. Association rules capture both structural and statistical information, and automatically identify the structures that occur most frequently and relationships that have significant discriminative power. So, association rules can be adapted to capture frequently occurring local structures in textures. This paper describes the usage of association rules for texture classification problem. The performed experimental studies show the effectiveness of the association rules. The overall success rate is about 98%.

Collaboration network of the authors

2 authors · 1 co-authorship link

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  • St. Joseph’s College of Engineering
  • Affiliation unknown
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  • Same organisation
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Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A Novel Texture Classification Procedure by using Association Rules</dc:title>
	<dc:creator>Sheela, L. Jaba</dc:creator>
	<dc:creator>Shanthi, V.</dc:creator>
	<dc:description xml:lang="en-US">Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. Association rules have been used in various applications during the past decades. Association rules capture both structural and statistical information, and automatically identify the structures that occur most frequently and relationships that have significant discriminative power. So, association rules can be adapted to capture frequently occurring local structures in textures. This paper describes the usage of association rules for texture classification problem. The performed experimental studies show the effectiveness of the association rules. The overall success rate is about 98%.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2013-09-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<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/177</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.2008.2.2.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 2 No. 2 (2008); 103-114</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/177/171</dc:relation>
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