Testing Landmark Salience Prediction in Indoor Environments Based on Visual Information

We identify automated landmark salience assessment in indoor environments as a problem related to pedestrian navigation systems that has not yet received much attention but is nevertheless of practical relevance. We therefore evaluate an approach based on visual information using images to capture the landmarks’ outward appearance. In this context we introduce the largest landmark image and salience value data set in the domain so far. We train various classifiers on domain agnostic visual features to predict the salience of landmarks. As a result, we are able to clarify the role of visual object features regarding perception of landmarks. Our results demonstrate that visual information has only limited expressiveness with respect to salience