Horizontal flip-invariant sketch recognition via local patch hashing

This paper introduces a flip aware patch matching frame-work that facilitates scalable sketch recognition. An overlapping spatial grid is utilized to generate an ensemble of patches for each sketch. We rank similarities between freely drawn sketches via a spatial voting process where similar patches in terms of shape and structure arbitrate for the result. Patch similarity is efficiently estimated via the min-hash algorithm. A novel spatial aware reverse index structure ensures the scalability of our scheme. We show the benefits of horizontal flip invariance and structural information in sketch recognition and demonstrate state-of-the-art results in two challenging sketch datasets.