  On the internet, images are no longer static; they have become dynamic
content. Thanks to the availability of smartphones with cameras and easy-to-use
editing software, images can be remixed (i.e., redacted, edited, and recombined
with other content) on-the-fly and with a world-wide audience that can repeat
the process. From digital art to memes, the evolution of images through time is
now an important topic of study for digital humanists, social scientists, and
media forensics specialists. However, because typical data sets in computer
vision are composed of static content, the development of automated algorithms
to analyze remixed content has been limited. In this paper, we introduce the
idea of Motif Mining - the process of finding and summarizing remixed image
content in large collections of unlabeled and unsorted data. In this paper,
this idea is formalized and a reference implementation is introduced.
Experiments are conducted on three meme-style data sets, including a newly
collected set associated with the information war in the Russo-Ukrainian
conflict. The proposed motif mining approach is able to identify related
remixed content that, when compared to similar approaches, more closely aligns
with the preferences and expectations of human observers.
