Image identification of Protea species with attributes and subgenus scaling

The flowering plant genus Protea is a dominant representative for the biodiversity of the Cape Floristic Region in South Africa, and from a conservation point of view important to monitor. The recent surge in popularity of crowd-sourced wildlife monitoring platforms presents both challenges and opportunities for automatic image based species identification. We consider the problem of identifying the Protea species in a given image with additional (but optional) attributes linked to the observation, such as location and date. We collect training and test data from a crowd-sourced platform, and find that the Protea identification problem is exacerbated by considerable inter-class similarity, data scarcity, class imbalance, as well as large variations in image quality, composition and background. Our proposed solution consists of three parts. The first part incorporates a variant of multi-region attention into a pretrained convolutional neural network, to focus on the flower-head in the image. The second part performs coarser-grained classification on subgenera (superclasses) and then rescales the output of the first part. The third part conditions a probabilistic model on the additional attributes associated with the observation. We perform an ablation study on the proposed model and its constituents, and find that all three components together outperform our baselines and all other variants quite significantly.