[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-216879-en":3,"doc-seo-216879-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},216879,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","IFSC/USP at ImageCLEF 2012 - Plant identification task - method and texture-based results","ImageCLEF 2012 runs a plant identification challenge focused on leaf analysis, and this paper documents the IFSC/USP team’s method for participating in the task. The approach explores multiple leaf attributes, combining geometric information from the leaf contour with fractal descriptors to characterize internal texture. The experiments demonstrate promising performance and highlight texture as the primary driver of improved leaf classification accuracy. Results support using computational image analysis to assist botanical identification year-round.","IFSC/USP at ImageCLEF 2012: Plant identi􀀌cation task  \nDalcimar Casanova? , Jo~ao Batista Florindo?? , Wesley Nunes Gon􀀘calves? ? ? ,  \nand Odemir Martinez Bruno  \nUSP-Universidade de S~ao Paulo  \nIFSC-Instituto de F􀀓􀀐sica de S~ao Carlos, S~ao Carlos, Brasil bruno@ifsc.usp.br  \nAbstract. ImageCLEF 2012 has a challenge based on leaf analysis for plant identi􀀌cation. This paper reports the method proposed by IFSC/USP team in the participation of this task. We try to explore several attributes (i.e. shape, location and texture) to make a system more accurate. The achieved results are promising and show as a principal outcome the power of texture on leaf analysis.  \nKeywords: Complex Network, Fractal, Taxonomy, Plant identi􀀌cation, Leaves.  \n1 Introduction  \nPlants identi􀀌cation has become an important and challenging research area since it is estimated that approximately one half of world plant species is still not cataloged. Among such unidenti􀀌ed species one may 􀀌nd, for instance, the healing of a disease or a plant that can cooperate in the equilibrium of the ecosystem around it. Despite the importance of studies related to the description and categorization of plants, this is still a di􀀎cult task for a botanist once this specialist still has a limited amount of information about the vegetal. Furthermore, among the information which may be collected, the most relevant for the botanist analysis are 􀀍owers and fruits. However, it turns out that in most cases these elements are observed only in speci􀀌c periods of the year. This is a complicated issue given that the observation may not be possible when these characteristics are noticeable.  \nA solution for this impasse is the use of the plant leaf. This structure uses tobe observed the whole year and can be collected in a straightforward manner.  \n? Dalcimar Casanova gratefully acknowledges the 􀀌nancial support FAPESP (S~ao Paulo Research Foundation, Brazil) (2008/57313-2) for his PhD grant.  \n?? Jo~ao Batista Florindo gratefully acknowledges the 􀀌nancial support CNPq (National Council for Scienti􀀌c and Technological Development) (870336/1997-5) for his PhD grant.  \n? ? ? Wesley Nunes Gon􀀘calves gratefully acknowledges the 􀀌nancial support FAPESP (S~ao Paulo Research Foundation, Brazil) (2010/08614-0) for his PhD grant.  \nNevertheless, most leaves lack more distinguishable attributes for a visual analysis. Thus, image analysis based on computational tools is a worthwhile approach in order to help the botanist or even provide by itself a reliable outcome for the classi􀀌cation task.  \nIn this context, ImageCLEF is a world campaign to encourage the development of novel strategies for the description and identi􀀌cation of objects, in this case, plant leaves, based on computational/mathematical techniques applied over digital images.  \nAs the group of this work has a signi􀀌cative background on computer vision techniques applied to plant identi􀀌cation [2], we decided to engage in this campaign and proposed a methodology combining complex networks and geometric features of the leaf contour in addition to fractal descriptors of the texture inside the leaf. These methods have already corroborated their e􀀎ciency on other works related to plant identi􀀌cation tasks [2] .  \nThis work is composed by 6 sections, including this introduction. The following section describes brie􀀍y the materials and methods employed in the experiments. The following one shows the experiments setup. The fourth section shows obtained results over the training data. The 􀀌fth one exhibits the results for the test data set while the last section presents the conclusions of the results.  \n2 Material and Methods  \n2.1 Database  \nThe experiments are performed over Pl@antLeaves dataset [5] . This database is maintained by the French project Pl@ntNet (INRIA, CIRAD, Telabotanica) . The full database contains 11572 images of 126 tree species. The images are taken under 3 di􀀋erent practical conditions:  \n1. Scan: contains 6630 s","cbCaijGLMx2GLahs","https://ap.wps.com/l/cbCaijGLMx2GLahs","pdf",181937,1,7,"English","en",105,"# Introduction\n## Material and Methods\n### Database\n### Pre-processing","[{\"question\":\"What problem does the ImageCLEF 2012 plant task address?\",\"answer\":\"It targets plant identification using leaf analysis, aiming to classify plant leaves from images.\"},{\"question\":\"What attributes does the proposed method use?\",\"answer\":\"The method combines geometric features from the leaf contour with fractal descriptors of the leaf’s internal texture, alongside other explored attributes such as shape and location.\"},{\"question\":\"Which feature is reported as most influential for performance?\",\"answer\":\"Texture on the leaf is identified as the principal outcome and key factor for more accurate analysis.\"}]","IFSC/USP at ImageCLEF 2012 - Plant identification task - method and texture-based results | PDF",1788828097,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"ifscusp-at-imageclef-2012-plant-identification-task-method-and-texture-based-results","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ifscusp-at-imageclef-2012-plant-identification-task-method-and-texture-based-results/216879/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-09-08",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the ImageCLEF 2012 plant task address?","Question",{"text":75,"@type":76},"It targets plant identification using leaf analysis, aiming to classify plant leaves from images.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What attributes does the proposed method use?",{"text":80,"@type":76},"The method combines geometric features from the leaf contour with fractal descriptors of the leaf’s internal texture, alongside other explored attributes such as shape and location.",{"name":82,"@type":73,"acceptedAnswer":83},"Which feature is reported as most influential for performance?",{"text":84,"@type":76},"Texture on the leaf is identified as the principal outcome and key factor for more accurate analysis.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]