Machine learning techniques for ontology-based leaf classification

Hong Fu, Zheru Chi, Dagan Feng, Jiatao Song

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

24 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Pages681-686
Number of pages6
Volume1
Publication statusPublished - 1 Dec 2004
Event8th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Kunming, China
Duration: 6 Dec 20049 Dec 2004

Conference

Conference8th International Conference on Control, Automation, Robotics and Vision (ICARCV)
CountryChina
CityKunming
Period6/12/049/12/04

ASJC Scopus subject areas

  • Engineering(all)

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