Harvesting Training Images for Fine-Grained Object Categories using Visual Descriptions

We harvest training images for visual object recognition by casting it as an IR task. In contrast to previous work, we concentrate on fine-grained object categories, such as the large number of particular animal subspecies, for which manual annotation is expensive. We use 'visual descriptions' from nature guides as a novel augmentation to the well-known use of category names. We use these descriptions in both the query process to find potential category images as well as in image reranking where an image is more highly ranked if web page text surrounding it is similar to the visual descriptions. We show the potential of this method when harvesting images for 10 butterfly categories: when compared to a method that relies on the category name only, using visual descriptions improves precision for many categories.