Learning landmark salience models from users’ route instructions

Abstract

Route instructions for pedestrians are usually better understood if they include references to landmarks, and moreover, these landmarks should be as salient as possible. In this paper, we present an approach for automatically deriving a mathematical model of salience directly from route instructions given by humans. Each possible landmark that a person can refer to in a given situation is modelled as a feature vector, and the salience associated with each landmark can be computed as a weighted sum of these features. We use a ranking SVM method to derive the weights from route instructions given by humans as they are walking the route. The weight vector, representing the person’s personal salience model, determines which landmark(s) are most appropriate to refer to in new situations.