Boosting for interactive man-made structure classification

We describe an interactive framework for man-made structure classification. Our system is able to help an image analyst to define a query that is adapted to various image and geographic contexts. It offers a GIS-like interface for visually selecting the training region samples and a fast and efficient sample description by histogram of oriented gradients and local binary patterns. To learn a discrimination rule in this feature space, our system relies on the online gradient-boost learning algorithm for which we defined a new family of loss functions. We chose non-convex loss-functions in order to be robust to mislabelling and proposed a generic way to incorporate prior information about the training data. We show it achieves better performances than other state-of-the-art machine-learning methods on various man-structure detection problems.