An Effective Plant Recognition Method with Feature Recalibration of Multiple Pretrained CNN and Layers

Current existing methods are either not very discriminative or too complex. In this work, an effective and very simple plant recognition method is proposed. The main innovations of our method are threefold. (1) The feature maps of multiple pretrained convolutional neural networks and multiple layers are extracted; the complementary information between different feature maps can be fully explored. (2) Performing spatial and channel feature recalibration on each feature map enables our method to highlight salient visual content and reduce non-salient content; as a result, more informative features can be discerned. (3) In contrast to conventional transfer learning with end-to-end network parameters fine-tuning, in our method one forward process is enough to extract discriminative features. All recalibrated features are concatenated to form the plant leaf representation, which is fed into a linear support vector machine classifier for recognition. Extensive experiments are carried out on eight representative plant databases, yielding outstanding recognition accuracies, which demonstrates the effectiveness and superiority of our method obviously. Moreover, the retrieval experiments show our method can offer higher or competitive mean average precisions compared with state-of-the-art method. The feature visualization shows our learned features have excellent intra-class similarity and inter-class diversity for leaf species from the same genus.