  This work addresses scaling up the sketch classification task into a large
number of categories. Collecting sketches for training is a slow and tedious
process that has so far precluded any attempts to large-scale sketch
recognition. We overcome the lack of training sketch data by exploiting labeled
collections of natural images that are easier to obtain. To bridge the domain
gap we present a novel augmentation technique that is tailored to the task of
learning sketch recognition from a training set of natural images.
Randomization is introduced in the parameters of edge detection and edge
selection. Natural images are translated to a pseudo-novel domain called
"randomized Binary Thin Edges" (rBTE), which is used as a training domain
instead of natural images. The ability to scale up is demonstrated by training
CNN-based sketch recognition of more than 2.5 times larger number of categories
than used previously. For this purpose, a dataset of natural images from 874
categories is constructed by combining a number of popular computer vision
datasets. The categories are selected to be suitable for sketch recognition. To
estimate the performance, a subset of 393 categories with sketches is also
collected.
