Two-dimensional multifractal detrended fluctuation analysis for plant identification
BACKGROUND: In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An...
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Veröffentlicht in: | Plant methods 2015-02, Vol.11 (1), p.12-12 |
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description | BACKGROUND: In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I₀, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species. RESULTS: The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species. Support vector machines and kernel methods are employed to assess the identification accuracy. The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 − fold cross validation, while the accuracy reaches 93.96% for all fifteen species. CONCLUSIONS: Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species. |
doi_str_mv | 10.1186/s13007-015-0049-7 |
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Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I₀, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species. RESULTS: The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species. Support vector machines and kernel methods are employed to assess the identification accuracy. The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 − fold cross validation, while the accuracy reaches 93.96% for all fifteen species. CONCLUSIONS: Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.</description><identifier>ISSN: 1746-4811</identifier><identifier>EISSN: 1746-4811</identifier><identifier>DOI: 10.1186/s13007-015-0049-7</identifier><identifier>PMID: 25774206</identifier><language>eng</language><publisher>England: Springer-Verlag</publisher><subject>data collection ; leaves ; Methodology ; plant identification ; support vector machines ; texture ; trees</subject><ispartof>Plant methods, 2015-02, Vol.11 (1), p.12-12</ispartof><rights>COPYRIGHT 2015 BioMed Central Ltd.</rights><rights>Wang et al.; licensee BioMed Central. 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b585t-9c6bdf9978b9192152b28f6a1c51ece4cc9206c29eb03ff23a89bdb2aca1472c3</citedby><cites>FETCH-LOGICAL-b585t-9c6bdf9978b9192152b28f6a1c51ece4cc9206c29eb03ff23a89bdb2aca1472c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4358846/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4358846/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,866,887,27931,27932,53798,53800</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25774206$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Fang</creatorcontrib><creatorcontrib>Liao, Deng-wen</creatorcontrib><creatorcontrib>Li, Jin-wei</creatorcontrib><creatorcontrib>Liao, Gui-ping</creatorcontrib><title>Two-dimensional multifractal detrended fluctuation analysis for plant identification</title><title>Plant methods</title><addtitle>Plant Methods</addtitle><description>BACKGROUND: In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I₀, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species. RESULTS: The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species. Support vector machines and kernel methods are employed to assess the identification accuracy. The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 − fold cross validation, while the accuracy reaches 93.96% for all fifteen species. CONCLUSIONS: Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.</description><subject>data collection</subject><subject>leaves</subject><subject>Methodology</subject><subject>plant identification</subject><subject>support vector machines</subject><subject>texture</subject><subject>trees</subject><issn>1746-4811</issn><issn>1746-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp1kktv1DAUhS0EomXgB7CBSGxgkWI7dmJvkKryqlQJiU7Xlu3Yg1ESD7YD9N9zh5SqkYq88ON-5-jeIyP0nOATQkT7NpMG467GhNcYM1l3D9Ax6VhbM0HIwzvnI_Qk5-_AENq0j9ER5V3HKG6P0Xb7K9Z9GN2UQ5z0UI3zUIJP2ha49K4kN_Wur_ww2zLrAlClgbvOIVc-pmo_6KlUoXcTyIL9SzxFj7wesnt2s2_Q1ccP27PP9cWXT-dnpxe14YKXWtrW9F7KThhJJCWcGip8q4nlxFnHrJXQo6XSGdx4TxstpOkN1VYT1lHbbNC7xXc_m9H1FnpIelD7FEadrlXUQa0rU_imdvGnYg0XgrVg8H4xMCH-x2BdsXFUS-oKUleH1FUHNq9v-kjxx-xyUWPI1g0QjYtzVqRtGZEMQ_ob9GpBd3pwKkw-gq894OqUM8J5AyBQJ_dQsHo3Bhsn5wO8rwRvVgJgivtddnrOWZ1ffl2zZGFtijkn52_nJVgd_tW9E764m_St4t9HAuDlAngdld6lkNXVJQUHjGF6IUTzB9Zl0zc</recordid><startdate>20150226</startdate><enddate>20150226</enddate><creator>Wang, Fang</creator><creator>Liao, Deng-wen</creator><creator>Li, Jin-wei</creator><creator>Liao, Gui-ping</creator><general>Springer-Verlag</general><general>BioMed Central Ltd</general><general>BioMed Central</general><scope>FBQ</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20150226</creationdate><title>Two-dimensional multifractal detrended fluctuation analysis for plant identification</title><author>Wang, Fang ; Liao, Deng-wen ; Li, Jin-wei ; Liao, Gui-ping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b585t-9c6bdf9978b9192152b28f6a1c51ece4cc9206c29eb03ff23a89bdb2aca1472c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>data collection</topic><topic>leaves</topic><topic>Methodology</topic><topic>plant identification</topic><topic>support vector machines</topic><topic>texture</topic><topic>trees</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Fang</creatorcontrib><creatorcontrib>Liao, Deng-wen</creatorcontrib><creatorcontrib>Li, Jin-wei</creatorcontrib><creatorcontrib>Liao, Gui-ping</creatorcontrib><collection>AGRIS</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Plant methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Fang</au><au>Liao, Deng-wen</au><au>Li, Jin-wei</au><au>Liao, Gui-ping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Two-dimensional multifractal detrended fluctuation analysis for plant identification</atitle><jtitle>Plant methods</jtitle><addtitle>Plant Methods</addtitle><date>2015-02-26</date><risdate>2015</risdate><volume>11</volume><issue>1</issue><spage>12</spage><epage>12</epage><pages>12-12</pages><issn>1746-4811</issn><eissn>1746-4811</eissn><abstract>BACKGROUND: In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I₀, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species. RESULTS: The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species. Support vector machines and kernel methods are employed to assess the identification accuracy. The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 − fold cross validation, while the accuracy reaches 93.96% for all fifteen species. CONCLUSIONS: Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.</abstract><cop>England</cop><pub>Springer-Verlag</pub><pmid>25774206</pmid><doi>10.1186/s13007-015-0049-7</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | data collection leaves Methodology plant identification support vector machines texture trees |
title | Two-dimensional multifractal detrended fluctuation analysis for plant identification |
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