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Using humans to build mid-level features

Identifying the distinctive parts of an image is a challenging task for computer vision. In contrast to previous methods, we use human participants to discover mid-level discriminative features. Amazon Mechanical Turk (AMT) workers filter groups of patches to identify clusters that have strong visual and semantic similarity. We show that SVMs trained from human-defined discriminative patches outperform the patch classifiers discovered by Singh et al. and Doersch et al. [7, 2] when used as features for classification on the 15 scene dataset [6].