Mango Leaf Classification with Boundary Moments of Centroid Contour Distances as Shape Features

Eko Prasetyo, R. Dimas Adityo, Nanik Suciati, Chastine Fatichah

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

6 Citations (Scopus)

Abstract

The previous research in mango leaf classification which used 270 features consisted of 256 texture features, 2 color features, and 2 shape features, could not achieve high classification performance. In this study, we conduct improvement by combining the previous features with the Boundary Moments of Centroid Contour Distance (CCD) and classify the combination features using Support Vector Machine with Linear and RBF kernels. The experiment results show that the combination features achieve higher classification performance compared to the previous features.

Original languageEnglish
Title of host publicationProceeding - 2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages317-320
Number of pages4
ISBN (Electronic)9781538676547
DOIs
Publication statusPublished - 2 Jul 2018
Event2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018 - Bali, Indonesia
Duration: 30 Aug 201831 Aug 2018

Publication series

NameProceeding - 2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018

Conference

Conference2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018
Country/TerritoryIndonesia
CityBali
Period30/08/1831/08/18

Keywords

  • Centroid Contour Distance
  • classification
  • combination features
  • mango leaf
  • shape features

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