TY - GEN
T1 - Weak constraint leaf image recognition based on convolutional neural network
AU - Kang, Euncheol
AU - Oh, Il Seok
N1 - Publisher Copyright:
© 2018 Institute of Electronics and Information Engineers.
PY - 2018/4/2
Y1 - 2018/4/2
N2 - Recently the computer vision and machine learning research communities pay a great attention to the leaf image recognition problem. Our literature survey focusing on the user interaction aspect reveals that two schemes of image acquisition have been used, one with strong constraint and the other with no constraint. The strong constraint interaction asks users to capture images by placing a leaf on a uniform background such as white paper while the unconstrained interaction allows any form of image capturing. The former one gets a high performance sacrificing the user convenience while the latter one provides a great convenience sacrificing the recognition performance. Our scheme is weakly constrained in the middle of two extremes. The proposed interaction scheme only asks users to center the leaf on smartphone camera screen. The leaf may be on the tree or off the tree. When the leaf is picked off the tree, it is recommended to place it against rather uniform background such as sky, soil, or tree bark. By fine-tuning the pre-trained CNNs (Convolutional Neural Network), we obtained a practical performance, 96.08% top-1 and 99.81% top-5 accuracies. The dataset is publicly open and the recognition system is released as an Android App.
AB - Recently the computer vision and machine learning research communities pay a great attention to the leaf image recognition problem. Our literature survey focusing on the user interaction aspect reveals that two schemes of image acquisition have been used, one with strong constraint and the other with no constraint. The strong constraint interaction asks users to capture images by placing a leaf on a uniform background such as white paper while the unconstrained interaction allows any form of image capturing. The former one gets a high performance sacrificing the user convenience while the latter one provides a great convenience sacrificing the recognition performance. Our scheme is weakly constrained in the middle of two extremes. The proposed interaction scheme only asks users to center the leaf on smartphone camera screen. The leaf may be on the tree or off the tree. When the leaf is picked off the tree, it is recommended to place it against rather uniform background such as sky, soil, or tree bark. By fine-tuning the pre-trained CNNs (Convolutional Neural Network), we obtained a practical performance, 96.08% top-1 and 99.81% top-5 accuracies. The dataset is publicly open and the recognition system is released as an Android App.
KW - Automatic leaf recognition
KW - convolutional neural network
KW - deep learning
KW - fine tuning
UR - https://www.scopus.com/pages/publications/85048485303
U2 - 10.23919/ELINFOCOM.2018.8330637
DO - 10.23919/ELINFOCOM.2018.8330637
M3 - Conference paper
AN - SCOPUS:85048485303
T3 - International Conference on Electronics, Information and Communication, ICEIC 2018
SP - 1
EP - 4
BT - International Conference on Electronics, Information and Communication, ICEIC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Electronics, Information and Communication, ICEIC 2018
Y2 - 24 January 2018 through 27 January 2018
ER -