  Semantic interaction (SI) in Dimension Reduction (DR) of images allows users
to incorporate feedback through direct manipulation of the 2D positions of
images. Through interaction, users specify a set of pairwise relationships that
the DR should aim to capture. Existing methods for images incorporate feedback
into the DR through feature weights on abstract embedding features. However, if
the original embedding features do not suitably capture the users' task then
the DR cannot either. We propose ImageSI, an SI method for image DR that
incorporates user feedback directly into the image model to update the
underlying embeddings, rather than weighting them. In doing so, ImageSI ensures
that the embeddings suitably capture the features necessary for the task so
that the DR can subsequently organize images using those features. We present
two variations of ImageSI using different loss functions - ImageSI_MDS_Inverse,
which prioritizes the explicit pairwise relationships from the interaction and
ImageSI_Triplet, which prioritizes clustering, using the interaction to define
groups of images. Finally, we present a usage scenario and a simulation based
evaluation to demonstrate the utility of ImageSI and compare it to current
methods.
