Abstract
Human free-hand sketches have been studied in various
contexts including sketch recognition, synthesis and finegrained sketch-based image retrieval (FG-SBIR). A fundamental challenge for sketch analysis is to deal with drastically different human drawing styles, particularly in terms
of abstraction level. In this work, we propose the first
stroke-level sketch abstraction model based on the insight
of sketch abstraction as a process of trading off between the
recognizability of a sketch and the number of strokes used
to draw it. Concretely, we train a model for abstract sketch
generation through reinforcement learning of a stroke removal policy that learns to predict which strokes can be
safely removed without affecting recognizability. We show
that our abstraction model can be used for various sketch
analysis tasks including: (1) modeling stroke saliency and
understanding the decision of sketch recognition models,
(2) synthesizing sketches of variable abstraction for a given
category, or reference object instance in a photo, and (3)
training a FG-SBIR model with photos only, bypassing the
expensive photo-sketch pair collection step