Dried jujube classification using support vector machine based on fractal parameters and red, green and blue intensity
Summary A new method that combines fractal theory and red, green and blue (RGB) colour intensity was developed to sort dried jujube fruits by using support vector machine (SVM). Our result shows that the new method is fast and accurate in dried jujube fruits classification. The SVM models based on f...
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| Veröffentlicht in: | International journal of food science & technology 2012-09, Vol.47 (9), p.1951-1957 |
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| container_end_page | 1957 |
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| container_issue | 9 |
| container_start_page | 1951 |
| container_title | International journal of food science & technology |
| container_volume | 47 |
| creator | Lou, Heqiang Hu, Ya Wang, Bin Lu, Hongfei |
| description | Summary
A new method that combines fractal theory and red, green and blue (RGB) colour intensity was developed to sort dried jujube fruits by using support vector machine (SVM). Our result shows that the new method is fast and accurate in dried jujube fruits classification. The SVM models based on fractal parameters only achieved 85.18–92.73% total accuracy rate. The total classification accuracy of SVM based on RGB intensity values was 94.44%. However, the SVM models based on combining fractal parameters with RGB intensity values achieved 94.44–98.15% total accuracy rate. The best classification accuracy (98.15%) was found when using SVM model based on combining fractal measures (FM) with RGB intensity values (C = 512, γ = 0.0078125). Therefore, the SVM model based on combining FMs with RGB intensity is recommended in dried jujube fruits classification. |
| doi_str_mv | 10.1111/j.1365-2621.2012.03055.x |
| format | Article |
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A new method that combines fractal theory and red, green and blue (RGB) colour intensity was developed to sort dried jujube fruits by using support vector machine (SVM). Our result shows that the new method is fast and accurate in dried jujube fruits classification. The SVM models based on fractal parameters only achieved 85.18–92.73% total accuracy rate. The total classification accuracy of SVM based on RGB intensity values was 94.44%. However, the SVM models based on combining fractal parameters with RGB intensity values achieved 94.44–98.15% total accuracy rate. The best classification accuracy (98.15%) was found when using SVM model based on combining fractal measures (FM) with RGB intensity values (C = 512, γ = 0.0078125). Therefore, the SVM model based on combining FMs with RGB intensity is recommended in dried jujube fruits classification.</description><identifier>ISSN: 0950-5423</identifier><identifier>EISSN: 1365-2621</identifier><identifier>DOI: 10.1111/j.1365-2621.2012.03055.x</identifier><identifier>CODEN: IJFTEZ</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Accuracy ; Biological and medical sciences ; Classification ; dried jujube fruits ; Flexible manufacturing systems ; Food industries ; Food science ; Fractal analysis ; fractal dimensions ; fractal measures ; Fractals ; Fruits ; Fundamental and applied biological sciences. Psychology ; green and blue ; Mathematical models ; red ; support vector machine ; Support vector machines</subject><ispartof>International journal of food science & technology, 2012-09, Vol.47 (9), p.1951-1957</ispartof><rights>2012 The Authors. International Journal of Food Science and Technology © 2012 Institute of Food Science and Technology</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5095-66666b49d7199612d69531e11cf8f5f06c37a3384bbff8067a927c3720fab41f3</citedby><cites>FETCH-LOGICAL-c5095-66666b49d7199612d69531e11cf8f5f06c37a3384bbff8067a927c3720fab41f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fj.1365-2621.2012.03055.x$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fj.1365-2621.2012.03055.x$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27922,27923,45572,45573</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26255899$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Lou, Heqiang</creatorcontrib><creatorcontrib>Hu, Ya</creatorcontrib><creatorcontrib>Wang, Bin</creatorcontrib><creatorcontrib>Lu, Hongfei</creatorcontrib><title>Dried jujube classification using support vector machine based on fractal parameters and red, green and blue intensity</title><title>International journal of food science & technology</title><description>Summary
A new method that combines fractal theory and red, green and blue (RGB) colour intensity was developed to sort dried jujube fruits by using support vector machine (SVM). Our result shows that the new method is fast and accurate in dried jujube fruits classification. The SVM models based on fractal parameters only achieved 85.18–92.73% total accuracy rate. The total classification accuracy of SVM based on RGB intensity values was 94.44%. However, the SVM models based on combining fractal parameters with RGB intensity values achieved 94.44–98.15% total accuracy rate. The best classification accuracy (98.15%) was found when using SVM model based on combining fractal measures (FM) with RGB intensity values (C = 512, γ = 0.0078125). Therefore, the SVM model based on combining FMs with RGB intensity is recommended in dried jujube fruits classification.</description><subject>Accuracy</subject><subject>Biological and medical sciences</subject><subject>Classification</subject><subject>dried jujube fruits</subject><subject>Flexible manufacturing systems</subject><subject>Food industries</subject><subject>Food science</subject><subject>Fractal analysis</subject><subject>fractal dimensions</subject><subject>fractal measures</subject><subject>Fractals</subject><subject>Fruits</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>green and blue</subject><subject>Mathematical models</subject><subject>red</subject><subject>support vector machine</subject><subject>Support vector machines</subject><issn>0950-5423</issn><issn>1365-2621</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNqNkUtv1DAUhSMEEkPhP1hCSCxI6mvHTrJBQoWWogISz6XlONfFIeMEOykz_75Op5oFK-7Gr-8cX52bZQRoAalO-wK4FDmTDApGgRWUUyGK3YNsc3x4mG1oI2guSsYfZ09i7CmljFflJrt5Gxx2pF_6pUViBh2js87o2Y2eLNH5axKXaRrDTG7QzGMgW21-OY-k1TEJE2WDNrMeyKSD3uKMIRLtOxKwe0WuA6K_O7bDgsT5GX108_5p9sjqIeKz-_Uk-37-7tvZ-_zq88Xl2Zur3IjUcS7Xasumq6BpJLBONoIDAhhbW2GpNLzSnNdl21pbU1nphlXpjlGr2xIsP8leHnynMP5ZMM5q66LBYdAexyUqoDVjIAWXCX3-D9qPS_CpOwUlh6YCAStVHygTxhgDWjUFt9Vhn6zUOhDVqzV3teau1oGou4GoXZK-uP9AR6OHFJs3Lh71SSBE3TSJe33g_roB9__try4_nH9dt8kgPxi4OOPuaKDDbyUrXgn189OFkhLkR_jxRTF-CwgarS0</recordid><startdate>201209</startdate><enddate>201209</enddate><creator>Lou, Heqiang</creator><creator>Hu, Ya</creator><creator>Wang, Bin</creator><creator>Lu, Hongfei</creator><general>Blackwell Publishing Ltd</general><general>Wiley-Blackwell</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7ST</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>SOI</scope></search><sort><creationdate>201209</creationdate><title>Dried jujube classification using support vector machine based on fractal parameters and red, green and blue intensity</title><author>Lou, Heqiang ; Hu, Ya ; Wang, Bin ; Lu, Hongfei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5095-66666b49d7199612d69531e11cf8f5f06c37a3384bbff8067a927c3720fab41f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>Biological and medical sciences</topic><topic>Classification</topic><topic>dried jujube fruits</topic><topic>Flexible manufacturing systems</topic><topic>Food industries</topic><topic>Food science</topic><topic>Fractal analysis</topic><topic>fractal dimensions</topic><topic>fractal measures</topic><topic>Fractals</topic><topic>Fruits</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>green and blue</topic><topic>Mathematical models</topic><topic>red</topic><topic>support vector machine</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lou, Heqiang</creatorcontrib><creatorcontrib>Hu, Ya</creatorcontrib><creatorcontrib>Wang, Bin</creatorcontrib><creatorcontrib>Lu, Hongfei</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><jtitle>International journal of food science & technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lou, Heqiang</au><au>Hu, Ya</au><au>Wang, Bin</au><au>Lu, Hongfei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dried jujube classification using support vector machine based on fractal parameters and red, green and blue intensity</atitle><jtitle>International journal of food science & technology</jtitle><date>2012-09</date><risdate>2012</risdate><volume>47</volume><issue>9</issue><spage>1951</spage><epage>1957</epage><pages>1951-1957</pages><issn>0950-5423</issn><eissn>1365-2621</eissn><coden>IJFTEZ</coden><abstract>Summary
A new method that combines fractal theory and red, green and blue (RGB) colour intensity was developed to sort dried jujube fruits by using support vector machine (SVM). Our result shows that the new method is fast and accurate in dried jujube fruits classification. The SVM models based on fractal parameters only achieved 85.18–92.73% total accuracy rate. The total classification accuracy of SVM based on RGB intensity values was 94.44%. However, the SVM models based on combining fractal parameters with RGB intensity values achieved 94.44–98.15% total accuracy rate. The best classification accuracy (98.15%) was found when using SVM model based on combining fractal measures (FM) with RGB intensity values (C = 512, γ = 0.0078125). Therefore, the SVM model based on combining FMs with RGB intensity is recommended in dried jujube fruits classification.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/j.1365-2621.2012.03055.x</doi><tpages>7</tpages></addata></record> |
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| subjects | Accuracy Biological and medical sciences Classification dried jujube fruits Flexible manufacturing systems Food industries Food science Fractal analysis fractal dimensions fractal measures Fractals Fruits Fundamental and applied biological sciences. Psychology green and blue Mathematical models red support vector machine Support vector machines |
| title | Dried jujube classification using support vector machine based on fractal parameters and red, green and blue intensity |
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