  In this paper we describe a new method for detecting and counting a repeating
object in an image. While the method relies on a fairly sophisticated
deformable part model, unlike existing techniques it estimates the model
parameters in an unsupervised fashion thus alleviating the need for a
user-annotated training data and avoiding the associated specificity. This
automatic fitting process is carried out by exploiting the recurrence of small
image patches associated with the repeating object and analyzing their spatial
correlation. The analysis allows us to reject outlier patches, recover the
visual and shape parameters of the part model, and detect the object instances
efficiently. In order to achieve a practical system which is able to cope with
diverse images, we describe a simple and intuitive active-learning procedure
that updates the object classification by querying the user on very few
carefully chosen marginal classifications. Evaluation of the new method against
the state-of-the-art techniques demonstrates its ability to achieve higher
accuracy through a better user experience.

Computer Vision and Pattern Recognition