Scene Parsing with Object Instances and Occlusion Ordering

TitleScene Parsing with Object Instances and Occlusion Ordering
Publication TypeConference Paper
Year of Publication2014
AuthorsTighe J, Niethammer M, Lazebnik S
Conference NameProceedings of the Conference on Computer Vision and Pattern Recognition (CVPR)

This work proposes a method to interpret a scene by assigning a semantic label at every pixel and inferring the spatial extent of individual object instances together with their occlusion relationships. Starting with an initial pixel labeling and a set of candidate object masks for a given test image, we select a subset of objects that explain the image well and have valid overlap relationships and occlusion ordering. This is done by minimizing an integer quadratic program either using a greedy method or a standard solver. Then we alternate between using the object predictions to improve the pixel labels and using the pixel labels to improve the object predictions. The proposed system obtains promising results on two challenging subsets of the LabelMe dataset, the largest of which contains 45,676 images and 232 classes.

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