Hierarchical Scene Annotation
Michael Maire, Stella X. Yu, Pietro Perona · 2013
We present a computer-assisted annotation system, together with a labeled dataset and benchmark suite, for evaluating an algorithm's ability to recover hierarchical scene structure.We evolve segmentation groundtruth from the two-dimensional image partition into a tree model that captures both occlusion and object-part relationships among possibly overlapping regions.Our tree model extends the segmentation problem to encompass object detection, object-part containment, and figure-ground ordering.We mitigate the cost of providing richer groundtruth labeling through a new webbased annotation tool with an intuitive graphical interface for rearranging the region hierarchy.Using precomputed superpixels, our tool also guides creation of user-specified regions with pixel-perfect boundaries.Widespread adoption of this human-machine combination should make the inaccuracies of bounding box labeling a relic of the past.Evaluating the state-of-the-art in fully automatic image segmentation reveals that it produces accurate two-dimension partitions, but does not respect groundtruth object-part structure.Our dataset and benchmark is the first to quantify these inadequacies.We illuminate recovery of rich scene structure as an important new goal for segmentation.