  Machine learning has achieved much success on supervised learning tasks with
large sets of well-annotated training samples. However, in many practical
situations, such strong and high-quality supervision provided by training data
is unavailable due to the expensive and labor-intensive labeling process.
Automatically identifying and recognizing object categories in a large volume
of unlabeled images with weak supervision remains an important, yet unsolved
challenge in computer vision. In this paper, we propose a novel machine
learning framework, artificial perceptual learning (APL), to tackle the problem
of weakly supervised image categorization. The proposed APL framework is
constructed using state-of-the-art machine learning algorithms as building
blocks to mimic the cognitive development process known as infant
categorization. We develop and illustrate the proposed framework by
implementing a wide-field fine-grain ecological survey of tree species over an
8,000-hectare area of the El Yunque rainforest in Puerto Rico. It is based on
unlabeled high-resolution aerial images of the tree canopy. Misplaced
ground-based labels were available for less than 1% of these images, which
serve as the only weak supervision for this learning framework. We validate the
proposed framework using a small set of images with high quality human
annotations and show that the proposed framework attains human-level cognitive
economy.
