Learning What Matters: Combining Probabilistic Models of 2D and 3D Saliency Cues

In this paper we address the problem of obtaining meaningful saliency measures that tie in coherently with other methods and modalities within larger robotic systems. We learn probabilistic models of various saliency cues from labeled training data and fuse these into probability maps, which while appearing to be qualitatively similar to traditional saliency maps, represent actual probabilities of detecting salient features. We show that these maps are better suited to pick up task-relevant structures in robotic applications. Moreover, having true probabilities rather than arbitrarily scaled saliency measures allows for deeper, semantically meaningful integration with other parts of the overall system.