Deep Learning of Path-Based Tree Classifiers for Large-Scale Plant Species Identification

Abstract

In this paper, a deep learning framework is developed to enable path-based tree classifier training for supporting large-scale plant species recognition, where a deep neural network and a tree classifier are jointly trained in an end-to-end fashion. First, a two-layer plant taxonomy is constructed to organize large numbers of plant species and their genus hierarchically in a coarse-to-fine fashion. Second, a deep learning framework is developed to enable path-based tree classifier training, where a tree classifier over the plant taxonomy is used to replace the flat softmax layer in traditional deep CNNs. A path-based error function is defined to optimize the joint process for learning deep CNN and tree classifier, where back propagation is used to update both the classifier parameters and the network weights simultaneously. We have also constructed a large-scale plant database of Orchid family for algorithm evaluation. Our experimental results have demonstrated that our path-based deep learning algorithm can achieve very competitive results on both the accuracy rates and the computational efficiency for large-scale plant species recognition.