Plant Leaf Identification via a Growing Convolution Neural Network with Progressive Sample Learning
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. The GCNN 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.
KeywordsSupport Vector Machine Recognition Rate Local Binary Pattern Support Vector Machine Classifier Sampling Layer
This research was supported by the National Natural Science Foundation of China (Nos. 61375047 and 61272366), the 973 Program of China (No. 2013CB329604), the 863 Program of China (No. 2012AA011005), the Program for Changjiang Scholars and Innovative Research Team in University of the Ministry of Education of China (No. IRT13059), the US National Science Foundation (NSF CCF-0905337), the Faculty Research Grant of Hong Kong Baptist University (No. FRG2/12-13/082), the Hong Kong Scholars Program (No. XJ2012012), China Postdoctoral Science Foundation (No. 2013M540510), and the Fundamental Research Funds for the Central Universities of China.
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