Abstract
| Original language | English |
|---|---|
| Title of host publication | 3rd International Conference on Information Technology, Information System and Electrical Engineering 2018 |
| Publisher | IEEE |
| Pages | 10-15 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5386-7082-8 |
| ISBN (Print) | 978-1-5386-7083-5 |
| DOIs | |
| Publication status | Published - 23 May 2019 |
| Externally published | Yes |
| Event | 3rd International Conference on Information Technology, Information System and Electrical Engineering 2018 - Grand Inna Malioboro Hotel, Yogyakarta, Indonesia Duration: 13 Nov 2018 → 14 Nov 2018 Conference number: 3 http://icitisee.amikompurwokerto.ac.id/ |
Conference
| Conference | 3rd International Conference on Information Technology, Information System and Electrical Engineering 2018 |
|---|---|
| Abbreviated title | ICITISEE 2018 |
| Country | Indonesia |
| City | Yogyakarta |
| Period | 13/11/18 → 14/11/18 |
| Internet address |
Fingerprint
Keywords
- image colour analysis
- feature extraction
- support vector machines
Cite this
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Comparison of color model for flower recognition. / Rosyani, Perani; Taufik, M; Waskita, Arya Adhyaksa; Apriyanti, Diah Harnoni .
3rd International Conference on Information Technology, Information System and Electrical Engineering 2018. IEEE, 2019. p. 10-15.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Comparison of color model for flower recognition
AU - Rosyani, Perani
AU - Taufik, M
AU - Waskita, Arya Adhyaksa
AU - Apriyanti, Diah Harnoni
PY - 2019/5/23
Y1 - 2019/5/23
N2 - 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.
AB - 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.
KW - image colour analysis
KW - feature extraction
KW - support vector machines
U2 - 10.1109/ICITISEE.2018.8721026
DO - 10.1109/ICITISEE.2018.8721026
M3 - Conference contribution
SN - 978-1-5386-7083-5
SP - 10
EP - 15
BT - 3rd International Conference on Information Technology, Information System and Electrical Engineering 2018
PB - IEEE
ER -