We present a new dataset of paired images and contour drawings for the study of visual understanding and sketch generation. In this dataset, there are 1,000 outdoor images and each is paired with 5 human drawings (5,000 drawings in total). The drawings have strokes roughly aligned for image boundaries, making it easier to correspond human strokes with image edges.
The dataset is collected with Amazon Mechanical Turk. The Turkers are
asked to trace over a fainted background image.
We demostrate a gaming interface for collecting large scale sketch dataset. This is inspired by the comments in the initial data collection phase, which state that making such drawings is an enjoyable process. Unlike boundary detection annotation, we only require a rough edge alignment and thus the task is much easier. This game will reward players when their strokes match some image edges and penalize otherwise. As a result, it encourages players to make high-quality drawings.
In order to obtain high-quality annotations, we design a labeling interface with a detailed instruction page including many positive and negative examples. The quality control is realized through manual inspection by treating annotations of the following types as rejection candidates: (1) missing inner boundary, (2) missing important objects, (3) with large misalignment with original edges, (4) the content not recognizable, (5) drawing humans with stick figures, (6) shaded on empty areas. Therefore, in addition to the 5,000 drawings accepted, we have 1,947 rejected submissions, which can be used in setting up an automatic quality guard.