Characterization of perceptual importance for object-based image segmentation

In this paper, we propose a machine learning approach for characterizing the perceptual importance of particular regions in an image. A modular neural network architecture is adapted for encoding our usual notion of a perceptually important region in such a way that generalization of this knowledge to previously unseen images is possible. Specifically, users are allowed to specify examples of perceptually significant regions in images, which are then incorporated as training data for the network. An important characteristic of this approach is its provision for grouping distinct regions into a single perceptually significant area through the previous user guidance, unlike conventional segmentation approaches which partition the image into homogeneous regions without further specifying the relationship between these regions.