The human visual system is just as good at recognizing objects in paintings and other abstract depictions as it is recognizing objects in their natural form. Computer vision methods can also recognize objects outside of natural images, however their model of the visual world may not always align with the human one. If the goal of computer vision is to mimic the human visual system, then we must strive to align detection models with the human one. We propose to use Picasso’s Cubist paintings to test whether detection methods mimic the human invariance to object fragmentation and part re-organization. We find that while humans significantly outperform current methods, human perception and part-based object models exhibit a similarly graceful degradation as abstraction increases, further corroborating the theory of part-based object representation in the brain.
National Science Foundation
Expeditions in Computing