Hierarchical Scene Annotation.

Supervised datasets play a central role as standards against which to benchmark long-term progress in computer vision. Over the past decade, the PASCAL [2] and Berkeley segmentation datasets (BSDS) [3] have filled these roles for the object detection and image segmentation tasks, respectively. The type of annotation available for each dataset determines the particular visual subtasks to which it is applicable. Object bounding boxes can benchmark detection algorithms, but are of limited use for training or evaluating segmentation. Segmented objects are more widely useful, but more time-consuming to annotate. What visual tasks are most important and what level of annotation detail is appropriate? We present an alternative to thinking about dataset annotation in terms of a restricted set of visual tasks. Our key observation is that a hierarchical groundtruth representation, in the form of a doubly ordered region tree, allows one to subsume disparate aspects of image labeling into a single framework. Specifically, we capture a nearly complete description of any scene in terms of objects, parts, object-part containment, segmentation, and figure-ground or occlusion ordering. Figure 1 illustrates the type of detail our annotation model encompasses for a typical scene. Our unifying abstraction regards a scene as a set S = {R1,R2, ...,Rn} where each Ri ⊆ I is a region in the image I. In general, Ri ∩R j may be nonempty. It then organizes regions {Ri} into a tree T . Let N(Ri) denote the node of T corresponding to region Ri. N(R j) is set to be the parent node of N(Ri) iff: (1) R j ⊃ Ri, (2) R j and Ri have an object-part relationship, and (3) @Rk : R j ⊃ Rk ⊃ Ri and R j, Rk, Ri have an objectpart-subpart relationship, respectively. If for Ri, no region R j satisfies all three conditions, then we set N(Ri) to be a child of the root node. Simply stated, T decomposes the the scene into a multilevel object-part hierarchy. We exploit one additional degree of freedom within T : the order O(·) in which nodes appear beneath a common parent encodes local occlusion relationships. Given sibling regions Ri and R j such that Ri∩R j 6= / 0, then Ri occludes R j if O(Ri) O(R j). If Ri ∩R j = / 0, then they do not occlude one another and we disregard their relative ordering. Given T and O, preorder tree traversal uses the objectpart hierarchy to translate local occlusions into a global figure-ground ordering. It also recovers a groundtruth ultrametric contour map (UCM) [1] weighting visible boundaries by the structural importance (object, part, subpart) of the regions they enclose. Figure 2 shows a partial scene tree for a self-occluding object. Our full paper describes extensions for representing loops (shirt both behind and in front of arm). To mitigate the cost of creating rich groundtruth, we introduce a web-based annotation tool with a graphical interface for managing the region hierarchy. Our software eliminates tedious tracing of region boundaries through a dynamic paintbrush that snaps to the shape of underlying superpixels in a precomputed oversegmentation. Combined with a touch-up mode, it guides fast creation of pixel-perfect regions. Figure 2: Our model of a scene or object groups pixels into regions and maps regions to nodes in a tree. Parent-child links denote region containment and semantic object-part relationships. Relative ordering of sibling nodes resolves occlusion ambiguities. Object-Part ⇒ ⇐ O c c lu s io n O rd e ri n g man