Leaf classification using marginalized shape context and shape+texture dual-path deep convolutional neural network

Identifying plant species based on photographs of their leaves is an important problem in computer vision and biology. Previous approaches for leaf image classification typically rely on hand-crafted shape features or texture features. In contrast, we propose a dual-path deep convolutional neural network (CNN) to (i) learn joint feature representations for leaf images, exploiting their shape and texture characteristics, and (ii) optimize these features for the classification task. We compare our CNN approach against (i) vanilla CNN classifiers and (ii) popular hand-crafted shape features, including a novel shape-context based feature that is extremely computationally efficient, which we call the marginalized shape context. Our results on three large public datasets demonstrate that our dual-path CNN leads to higher accuracy and consistency than the state of the art.