Scale Invariant Texture Analysis Using Multi-scale Local Autocorrelation Features
We have developed a new framework for scale invariant texture analysis using multi-scale local autocorrelation features. The multi-scale features are made of concatenated feature vectors of different scales, which are calculated from higher-order local autocorrelation functions. To classify differen...
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| creator | Kang, Yousun Morooka, Ken’ichi Nagahashi, Hiroshi |
| description | We have developed a new framework for scale invariant texture analysis using multi-scale local autocorrelation features. The multi-scale features are made of concatenated feature vectors of different scales, which are calculated from higher-order local autocorrelation functions. To classify different types of textures among the given test images, a linear discriminant classifier (LDA) is employed in the multi-scale feature space. The scale rate of test patterns in their reduced subspace can also be estimated by principal component analysis (PCA). This subspace represents the scale variation of each scale step by principal components of a training texture image. Experimental results show that the proposed method is effective in not only scale invariant texture classification including estimation of scale rate, but also scale invariant segmentation of 2D image for scene analysis. |
| doi_str_mv | 10.1007/11408031_31 |
| format | Conference Proceeding |
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The multi-scale features are made of concatenated feature vectors of different scales, which are calculated from higher-order local autocorrelation functions. To classify different types of textures among the given test images, a linear discriminant classifier (LDA) is employed in the multi-scale feature space. The scale rate of test patterns in their reduced subspace can also be estimated by principal component analysis (PCA). This subspace represents the scale variation of each scale step by principal components of a training texture image. Experimental results show that the proposed method is effective in not only scale invariant texture classification including estimation of scale rate, but also scale invariant segmentation of 2D image for scene analysis.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540255475</identifier><identifier>ISBN: 3540255478</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540320121</identifier><identifier>EISBN: 9783540320128</identifier><identifier>DOI: 10.1007/11408031_31</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Feature Vector ; Linear Discriminant Analysis ; Pattern recognition. Digital image processing. Computational geometry ; Texture Gradient ; Texture Image ; Texture Pattern</subject><ispartof>Lecture notes in computer science, 2005, p.363-373</ispartof><rights>Springer-Verlag Berlin Heidelberg 2005</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11408031_31$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11408031_31$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,775,776,780,785,786,789,27904,38234,41421,42490</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16894708$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Sochen, Nir A.</contributor><contributor>Weickert, Joachim</contributor><contributor>Kimmel, Ron</contributor><creatorcontrib>Kang, Yousun</creatorcontrib><creatorcontrib>Morooka, Ken’ichi</creatorcontrib><creatorcontrib>Nagahashi, Hiroshi</creatorcontrib><title>Scale Invariant Texture Analysis Using Multi-scale Local Autocorrelation Features</title><title>Lecture notes in computer science</title><description>We have developed a new framework for scale invariant texture analysis using multi-scale local autocorrelation features. The multi-scale features are made of concatenated feature vectors of different scales, which are calculated from higher-order local autocorrelation functions. To classify different types of textures among the given test images, a linear discriminant classifier (LDA) is employed in the multi-scale feature space. The scale rate of test patterns in their reduced subspace can also be estimated by principal component analysis (PCA). This subspace represents the scale variation of each scale step by principal components of a training texture image. Experimental results show that the proposed method is effective in not only scale invariant texture classification including estimation of scale rate, but also scale invariant segmentation of 2D image for scene analysis.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Feature Vector</subject><subject>Linear Discriminant Analysis</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Texture Gradient</subject><subject>Texture Image</subject><subject>Texture Pattern</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540255475</isbn><isbn>3540255478</isbn><isbn>3540320121</isbn><isbn>9783540320128</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkDtPw0AQhI-XRAip-ANuKCgMew_fo4wiApGCECLUp419FxnMObpzEPn32ISCbaaYb1c7Q8gVhVsKoO4oFaCBU8vpEbnghQDOgDJ6TEZUUppzLswJmRilB48VhVDFKRkBB5YbJfg5maT0Dv1wagyYEXl5LbFx2SJ8YawxdNnKfXe76LJpwGaf6pS9pTpssqdd09V5-oWXbS_ZdNe1ZRuja7Cr25DNHQ6L6ZKceWySm_zpmKzm96vZY758fljMpss8MMm7HKWQa73mEpVAcIYVJZrSe6EkE64UuqoEA6856zNoo6Xx6J2DSknPq4KPyfXh7BaHr3zEUNbJbmP9iXFvqdRGKNA9d3PgUm-FjYt23bYfyVKwQ6X2X6X8B7MDY3I</recordid><startdate>20050101</startdate><enddate>20050101</enddate><creator>Kang, Yousun</creator><creator>Morooka, Ken’ichi</creator><creator>Nagahashi, Hiroshi</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>20050101</creationdate><title>Scale Invariant Texture Analysis Using Multi-scale Local Autocorrelation Features</title><author>Kang, Yousun ; Morooka, Ken’ichi ; Nagahashi, Hiroshi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-n263t-a646b8b36a74a0e925ca9cff47624ec48dd420f83247589869fafee0d76f3d53</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Feature Vector</topic><topic>Linear Discriminant Analysis</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Texture Gradient</topic><topic>Texture Image</topic><topic>Texture Pattern</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kang, Yousun</creatorcontrib><creatorcontrib>Morooka, Ken’ichi</creatorcontrib><creatorcontrib>Nagahashi, Hiroshi</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kang, Yousun</au><au>Morooka, Ken’ichi</au><au>Nagahashi, Hiroshi</au><au>Sochen, Nir A.</au><au>Weickert, Joachim</au><au>Kimmel, Ron</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Scale Invariant Texture Analysis Using Multi-scale Local Autocorrelation Features</atitle><btitle>Lecture notes in computer science</btitle><date>2005-01-01</date><risdate>2005</risdate><spage>363</spage><epage>373</epage><pages>363-373</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540255475</isbn><isbn>3540255478</isbn><eisbn>3540320121</eisbn><eisbn>9783540320128</eisbn><abstract>We have developed a new framework for scale invariant texture analysis using multi-scale local autocorrelation features. The multi-scale features are made of concatenated feature vectors of different scales, which are calculated from higher-order local autocorrelation functions. To classify different types of textures among the given test images, a linear discriminant classifier (LDA) is employed in the multi-scale feature space. The scale rate of test patterns in their reduced subspace can also be estimated by principal component analysis (PCA). This subspace represents the scale variation of each scale step by principal components of a training texture image. Experimental results show that the proposed method is effective in not only scale invariant texture classification including estimation of scale rate, but also scale invariant segmentation of 2D image for scene analysis.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11408031_31</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2005, p.363-373 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16894708 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Feature Vector Linear Discriminant Analysis Pattern recognition. Digital image processing. Computational geometry Texture Gradient Texture Image Texture Pattern |
| title | Scale Invariant Texture Analysis Using Multi-scale Local Autocorrelation Features |
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