Leaf-based plant identification through morphological characterization in digital images

Arturo Oncevay-Marcos, Ronald Juarez-Chambi, Sofía Khlebnikov-Núñez, César Beltráón-Castañn

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

The plant species identification is a manual process performed mainly by botanical scientists based on their experience. In order to improve this task, several plant classification processes has been proposed applying pattern recognition. In this work, we propose a method combining three visual attributes of leaves: boundary shape, texture and color. Complex networks and multi-scale fractal dimension techniques were used to characterize the leaf boundary shape, the Haralick’s descriptors for texture were extracted, and color moments were calculated. Experiments were performed on the ImageCLEF 2012 train dataset, scan pictures only. We reached up to 90.41% of accuracy regarding the leafbased plant identification problem for 115 species.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 16th International Conference, CAIP 2015, Proceedings
EditorsGeorge Azzopardi, Nicolai Petkov
PublisherSpringer Verlag
Pages326-335
Number of pages10
ISBN (Print)9783319231167
DOIs
StatePublished - 2015
Event16th International Conference on Computer Analysis of Images and Patterns, CAIP 2015 - Valletta, Malta
Duration: 2 Sep 20154 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9257
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Computer Analysis of Images and Patterns, CAIP 2015
Country/TerritoryMalta
CityValletta
Period2/09/154/09/15

Keywords

  • Color moments
  • Complex networks
  • Haralick’s descriptors
  • Leaf-based plant identification
  • Multiscale fractal dimension

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