  Treating images as data has become increasingly popular in political science.
While existing classifiers for images reach high levels of accuracy, it is
difficult to systematically assess the visual features on which they base their
classification. This paper presents a two-level classification method that
addresses this transparency problem. At the first stage, an image segmenter
detects the objects present in the image and a feature vector is created from
those objects. In the second stage, this feature vector is used as input for
standard machine learning classifiers to discriminate between images. We apply
this method to a new dataset of more than 140,000 images to detect which ones
display political protest. This analysis demonstrates three advantages to this
paper's approach. First, identifying objects in images improves transparency by
providing human-understandable labels for the objects shown on an image.
Second, knowing these objects enables analysis of which distinguish protest
images from non-protest ones. Third, comparing the importance of objects across
countries reveals how protest behavior varies. These insights are not available
using conventional computer vision classifiers and provide new opportunities
for comparative research.
