Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1895
Title: Machine learning techniques for ontology-based leaf classification
Authors: Fu, H
Chi, ZG 
Feng, DD
Song, J
Keywords: Botany
Feature extraction
Image classification
Image retrieval
Image texture
Learning (artificial intelligence)
Neural nets
Ontologies (artificial intelligence)
Issue Date: 2004
Publisher: IEEE
Source: 2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV) : Kunming, China, 6-9 December 2004, v. 1, p. 681-686 How to cite?
Abstract: Leaf 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.
URI: http://hdl.handle.net/10397/1895
ISBN: 0-7803-8653-1
DOI: 10.1109/ICARCV.2004.1468909
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.
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