TY - GEN
T1 - Plant leaf identification via a growing convolution neural network with progressive sample learning
AU - Zhao, Zhong Qiu
AU - Xie, Bao Jian
AU - CHEUNG, Yiu Ming
AU - Wu, Xindong
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Plant identification is an important problem for ecologists, amateur botanists, educators, and so on. Leaf, which can be easily obtained, is usually one of the important factors of plants. In this paper, we propose a growing convolution neural network (GCNN) for plant leaf identification and report the promising results on the ImageCLEF2012 Plant Identification database. TheGCNN owns a growing structure which starts training from a simple structure of a single convolution kernel and is gradually added new convolution neurons to. Simultaneously, the growing connection weights are modified until the squared-error achieves the desired result. Moreover, we propose a progressive learning method to determine the number of learning samples, which can further improve the recognition rate. Experiments and analyses show that our proposed GCNN outperforms other state-of-the-art algorithms such as the traditional CNN and the hand-crafted features with SVM classifiers.
AB - Plant identification is an important problem for ecologists, amateur botanists, educators, and so on. Leaf, which can be easily obtained, is usually one of the important factors of plants. In this paper, we propose a growing convolution neural network (GCNN) for plant leaf identification and report the promising results on the ImageCLEF2012 Plant Identification database. TheGCNN owns a growing structure which starts training from a simple structure of a single convolution kernel and is gradually added new convolution neurons to. Simultaneously, the growing connection weights are modified until the squared-error achieves the desired result. Moreover, we propose a progressive learning method to determine the number of learning samples, which can further improve the recognition rate. Experiments and analyses show that our proposed GCNN outperforms other state-of-the-art algorithms such as the traditional CNN and the hand-crafted features with SVM classifiers.
UR - http://www.scopus.com/inward/record.url?scp=84945959664&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16808-1_24
DO - 10.1007/978-3-319-16808-1_24
M3 - Conference proceeding
AN - SCOPUS:84945959664
SN - 9783319168074
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 348
EP - 361
BT - Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Yang, Ming-Hsuan
A2 - Saito, Hideo
A2 - Cremers, Daniel
A2 - Reid, Ian
PB - Springer Verlag
T2 - 12th Asian Conference on Computer Vision, ACCV 2014
Y2 - 1 November 2014 through 5 November 2014
ER -