Using Agents and Unsupervised Learning for Counting Objects in Images with Spatial Organization

This paper addresses the problem of counting objects from aerial images. Classical approaches either consider the task as a regression problem or view it as a recognition problem of the objects in a sliding window over the images, with, in each case, the need of a lot of labeled images and careful adjustments of the parameters of the learning algorithm. Instead of using a supervised learning approach, the proposed method uses unsupervised learning and an agent-based technique which relies on prior detection of the relationships among objects. The method is demonstrated on the problem of counting plants where it achieves state of the art performance when the objects are well separated and tops the best known performances when the objects overlap. The description of the method underlines its generic nature as it could also be used to count objects organized in a geometric pattern, such as spectators in a performance hall.