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Unsupervised discovery of visual object class hierarchies
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
Objects in the world can be arranged into a hierarchy based on their semantic meaning (e.g. organism -animal -feline -cat). What about defining a hierarchy based on the visual appearance of objects? This paper investigates ways to automatically discover a hierarchical structure for the visual world from a collection of unlabeled images. Previous approaches for unsupervised object and scene discovery focused on partitioning the visual data into a set of nonoverlapping classes of equal
doi:10.1109/cvpr.2008.4587622
dblp:conf/cvpr/SivicRZFE08
fatcat:hbwnebsxmfbv3ohazsjhjmotu4