TY - GEN
T1 - Flower classification based on single petal image and machine learning methods
AU - Lu, Siyuan
AU - Lu, Zhihai
AU - Chen, Xianqing
AU - Wang, Shuihua
AU - Zhang, Yudong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/21
Y1 - 2018/6/21
N2 - This research presented a novel automatic flower classification system based on computer vision and machine learning techniques. First, we obtained in total 157 petal images of three alike categories using a digital camera. After pre-processing, we extracted color features and wavelet entropies from the petal images. Then, principle component analysis was utilized for feature reduction. Finally, four different classifiers, Support Vector Machine, Weighted k Nearest Neighbors, Kernel based Extreme Learning Machine, and Decision Tree, were trained to recognize the categories of the petals. 5-fold cross validation was employed to evaluate the out-of-sample performance of the classifiers. The experimental results showed that Weighted k-Nearest Neighbors performed the best among all four classifiers with an overall accuracy of 99.4%. The proposed approach is efficient in identifying flower categories in comparison with state-of-the-art methods.
AB - This research presented a novel automatic flower classification system based on computer vision and machine learning techniques. First, we obtained in total 157 petal images of three alike categories using a digital camera. After pre-processing, we extracted color features and wavelet entropies from the petal images. Then, principle component analysis was utilized for feature reduction. Finally, four different classifiers, Support Vector Machine, Weighted k Nearest Neighbors, Kernel based Extreme Learning Machine, and Decision Tree, were trained to recognize the categories of the petals. 5-fold cross validation was employed to evaluate the out-of-sample performance of the classifiers. The experimental results showed that Weighted k-Nearest Neighbors performed the best among all four classifiers with an overall accuracy of 99.4%. The proposed approach is efficient in identifying flower categories in comparison with state-of-the-art methods.
KW - K-nearest neighbors
KW - decision tree
KW - flower classification
KW - kernel based extreme learning machine
KW - support vector machine
KW - wavelet entropy
UR - http://www.scopus.com/inward/record.url?scp=85050182706&partnerID=8YFLogxK
U2 - 10.1109/FSKD.2017.8393382
DO - 10.1109/FSKD.2017.8393382
M3 - Conference Proceeding
AN - SCOPUS:85050182706
T3 - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
SP - 826
EP - 831
BT - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
A2 - Zhao, Liang
A2 - Wang, Lipo
A2 - Cai, Guoyong
A2 - Li, Kenli
A2 - Liu, Yong
A2 - Xiao, Guoqing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Y2 - 29 July 2017 through 31 July 2017
ER -