Comparison of color model for flower recognition

Perani Rosyani, M Taufik, Arya Adhyaksa Waskita, Diah Harnoni Apriyanti

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

Abstract

Comparison to RGB, HSV, LAB and YCbCr color model in a flower recognition has been conducted for 12 species from two families. Flower images were obtained from the ImageCLEF 2017 dataset. After segmenting the flower from its background using the k-means clustering method, the statistical parameters are extracted from each color model. These parameters including the minimum and maximum pixel value, and also mean and standard deviation. Then, the classifying result of SVM shows that the HSV color model performs the worst amongst the other investigated color model, which is around 30% using different kernel. While the other color model has the accuracy around 70% using linear and polynomial kernel. This result in line with the surface plot of their statistical characteristics.
Original languageEnglish
Title of host publication3rd International Conference on Information Technology, Information System and Electrical Engineering 2018
PublisherIEEE
Pages10-15
Number of pages6
ISBN (Electronic)978-1-5386-7082-8
ISBN (Print)978-1-5386-7083-5
DOIs
Publication statusPublished - 23 May 2019
Externally publishedYes
Event3rd International Conference on Information Technology, Information System and Electrical Engineering 2018 - Grand Inna Malioboro Hotel, Yogyakarta, Indonesia
Duration: 13 Nov 201814 Nov 2018
Conference number: 3
http://icitisee.amikompurwokerto.ac.id/

Conference

Conference3rd International Conference on Information Technology, Information System and Electrical Engineering 2018
Abbreviated titleICITISEE 2018
CountryIndonesia
CityYogyakarta
Period13/11/1814/11/18
Internet address

Keywords

  • image colour analysis
  • feature extraction
  • support vector machines

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  • Cite this

    Rosyani, P., Taufik, M., Waskita, A. A., & Apriyanti, D. H. (2019). Comparison of color model for flower recognition. In 3rd International Conference on Information Technology, Information System and Electrical Engineering 2018 (pp. 10-15). IEEE. https://doi.org/10.1109/ICITISEE.2018.8721026