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Fine-grained bird species recognition via hierarchical subset learning

We propose a novel method to improve fine-grained bird species classification based on hierarchical subset learning. We first form a similarity tree where classes with strong visual correlations are grouped into subsets. An expert local classifier with strong discriminative power to distinguish visually similar classes is then learnt for each subset. On the challenging Caltech200-2011 bird dataset we show that using the hierarchical approach with features derived from a deep convolutional neural network leads to the average accuracy improving from 64.5% to 72.7%, a relative improvement of 12.7%.