Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1895
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorFu, H-
dc.creatorChi, ZG-
dc.creatorFeng, DD-
dc.creatorSong, J-
dc.date.accessioned2014-12-11T08:26:45Z-
dc.date.available2014-12-11T08:26:45Z-
dc.identifier.isbn0-7803-8653-1-
dc.identifier.urihttp://hdl.handle.net/10397/1895-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectBotanyen_US
dc.subjectFeature extractionen_US
dc.subjectImage classificationen_US
dc.subjectImage retrievalen_US
dc.subjectImage textureen_US
dc.subjectLearning (artificial intelligence)en_US
dc.subjectNeural netsen_US
dc.subjectOntologies (artificial intelligence)en_US
dc.titleMachine learning techniques for ontology-based leaf classificationen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: Zheru Chien_US
dc.description.otherinformationAuthor name used in this publication: Dagan Fengen_US
dc.description.otherinformationCentre for Multimedia Signal Processing, Department of Electronic and Information Engineeringen_US
dc.description.otherinformationRefereed conference paperen_US
dc.identifier.doi10.1109/ICARCV.2004.1468909-
dcterms.abstractLeaf classification, indexing as well as retrieval is an important part of a computerized plant identification system. In this paper, an integrated approach for an ontology-based leaf classification system is proposed, wherein machine learning techniques play a crucial role for the automatization of the system. For the leaf contour classification, a scaled CCD code system is proposed to categorize the basic shape and margin type of a leaf by using the similar taxonomy principle adopted by the botanists. Then a trained neural network is employed to recognize the detailed tooth patterns. The measurement on an unlobed leaf is also conducted automatically according to the method used in botany. For the leaf vein recognition, the vein texture is extracted by employing an efficient combined thresholding and neural network approach so as to obtain more vein details of a leaf. Compared with the past studies, the proposed method integrates low-level features of an image and the specific knowledge in the domain (ontology) of botany, and therefore provides a more practical system for users to comprehend and handle. Primary experiments have shown promising results and proven the feasibility of the proposed system.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV) : Kunming, China, 6-9 December 2004, v. 1, p. 681-686-
dcterms.issued2004-
dc.identifier.isiWOS:000230484500122-
dc.identifier.scopus2-s2.0-21244435571-
dc.identifier.rosgroupidr23576-
dc.description.ros2004-2005 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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