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Contour Drawing
Others
Aesthetics
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License: CC BY-NC-SA 4.0

Overview

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.

Data Collection

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.

Data Annotation

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.

License

CC BY-NC-SA 4.0

Data Summary
Type
Image,
Amount
--
Size
320.07MB
Provided by
MengtianLi et al.
A Ph.D. student (2017-) at the Robotics Insitute of Carnegie Mellon University
| Amount -- | Size 320.07MB
Contour Drawing
Others
Aesthetics
License: CC BY-NC-SA 4.0

Overview

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.

Data Collection

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.

Data Annotation

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.

License

CC BY-NC-SA 4.0

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