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This paper investigates the natural bias humans display when labeling images with a container label like vehicle or carnivore. Using three container concepts as subtree root nodes, and all available concepts between these roots and the images from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset, we analyze the differences between the images labeled at these varying levels of abstraction and the union of their constituting leaf nodes. We find that for many containerdoi:10.1145/2324796.2324806 dblp:conf/mir/VreeswijkSSS12 fatcat:ht5u5g4nibep5ctawmtboms5vu