  Illustrations are an essential transmission instrument. For an historian, the
first step in studying their evolution in a corpus of similar manuscripts is to
identify which ones correspond to each other. This image collation task is
daunting for manuscripts separated by many lost copies, spreading over
centuries, which might have been completely re-organized and greatly modified
to adapt to novel knowledge or belief and include hundreds of illustrations.
Our contributions in this paper are threefold. First, we introduce the task of
illustration collation and a large annotated public dataset to evaluate
solutions, including 6 manuscripts of 2 different texts with more than 2 000
illustrations and 1 200 annotated correspondences. Second, we analyze state of
the art similarity measures for this task and show that they succeed in simple
cases but struggle for large manuscripts when the illustrations have undergone
very significant changes and are discriminated only by fine details. Finally,
we show clear evidence that significant performance boosts can be expected by
exploiting cycle-consistent correspondences. Our code and data are available on
http://imagine.enpc.fr/~shenx/ImageCollation.
