Intelligent Image Collection: Building the Optimal Dataset

Key recognition tasks such as fine-grained visual categorization (FGVC) have benefited from increasing attention among computer vision researchers. The development and evaluation of new approaches relies heavily on benchmark datasets; such datasets are generally built primarily with categories that have images readily available, omitting categories with insufficient data. This paper takes a step back and rethinks dataset construction, focusing on intelligent image collection driven by: (i) the inclusion of all desired categories, and, (ii) the recognition performance on those categories. Based on a small, author-provided initial dataset, the proposed system recommends which categories the authors should prioritize collecting additional images for, with the intent of optimizing overall categorization accuracy. We show that mock datasets built using this method outperform datasets built without such a guiding framework. Additional experiments give prospective dataset creators intuition into how, based on their circumstances and goals, a dataset should be constructed.